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1-hop neighbor's text information: Comparison of Regression Methods, Symbolic Induction Methods and Neural Networks in Morbidity Diagnosis and Mortality: Classifier induction algorithms differ on what inductive hypotheses they can represent, and on how they search their space of hypotheses. No classifier is better than another for all problems: they have selective superiority. This paper empirically compares six classifier induction algorithms on the diagnosis of equine colic and the prediction of its mortality. The classification is based on simultaneously analyzing sixteen features measured from a patient. The relative merits of the algorithms (linear regression, decision trees, nearest neighbor classifiers, the Model Class Selection system, logistic regression (with and without feature selection), and neural nets) are qualitatively discussed, and the generalization accuracies quantitatively analyzed.
1-hop neighbor's text information: An empirical comparison of selection measures for decision-tree induction. : Ourston and Mooney, 1990b ] D. Ourston and R. J. Mooney. Improving shared rules in multiple category domain theories. Technical Report AI90-150, Artificial Intelligence Labora tory, University of Texas, Austin, TX, December 1990.
1-hop neighbor's text information: Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1): This is a brief annotated bibliography that I wanted to make available to the attendees of my Machine Learning tutorial at the 1995 AI & Statistics Workshop. These slides are available in my WWW pages under slides. Please contact me if you have any questions. Please also note the date (listed above) on which this was most recently updated. While I plan to make occasional updates to this file, it is bound to be outdated quickly. Also, I apologize for the lack of figures, but my time on this project is limited and the slides should compensate. Finally, this bibliography is, by definition, This book is now out of date. Both Pat Langley and Tom Mitchell are in the process of writing textbooks on this subject, but we're still waiting for them. Until then, I suggest looking at both the Readings and the recent ML conference proceedings (both International and European). There are also a few introductory papers on this subject, though I haven't gotten around to putting them in here yet. However, Pat Langley and Dennis Kibler (1988) have written a good paper on ML as an empirical science, and Pat has written several editorials of use to the ML author (Langley 1986; 1987; 1990). incomplete, and I've left out many other references that may be of some use.
Target text information: Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection. : COINS Technical Report 92-30 February 1992 Abstract The problem of how to learn from examples has been studied throughout the history of machine learning, and many successful learning algorithms have been developed. A problem that has received less attention is how to select which algorithm to use for a given learning task. The ability of a chosen algorithm to induce a good generalization depends on how appropriate the model class underlying the algorithm is for the given task. We define an algorithm's model class to be the representation language it uses to express a generalization of the examples. Supervised learning algorithms differ in their underlying model class and in how they search for a good generalization. Given this characterization, it is not surprising that some algorithms find better generalizations for some, but not all tasks. Therefore, in order to find the best generalization for each task, an automated learning system must search for the appropriate model class in addition to searching for the best generalization within the chosen class. This thesis proposal investigates the issues involved in automating the selection of the appropriate model class. The presented approach has two facets. Firstly, the approach combines different model classes in the form of a model combination decision tree, which allows the best representation to be found for each subconcept of the learning task. Secondly, which model class is the most appropriate is determined dynamically using a set of heuristic rules. Explicit in each rule are the conditions in which a particular model class is appropriate and if it is not, what should be done next. In addition to describing the approach, this proposal describes how the approach will be evaluated in order to demonstrate that it is both an efficient and effective method for automatic model selection.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Evolution of non-deterministic incremental algorithms as a new approach for search in state spaces. : Let us call a non-deterministic incremental algorithm one that is able to construct any solution to a combinatorial problem by selecting incrementally an ordered sequence of choices that defines this solution, each choice being made non-deterministically. In that case, the state space can be represented as a tree, and a solution is a path from the root of that tree to a leaf. This paper describes how the simulated evolution of a population of such non-deterministic incremental algorithms offers a new approach for the exploration of a state space, compared to other techniques like Genetic Algorithms (GA), Evolutionary Strategies (ES) or Hill Climbing. In particular, the efficiency of this method, implemented as the Evolving Non-Determinism (END) model, is presented for the sorting network problem, a reference problem that has challenged computer science. Then, we shall show that the END model remedies some drawbacks of these optimization techniques and even outperforms them for this problem. Indeed, some 16-input sorting networks as good as the best known have been built from scratch, and even a 25-year-old result for the 13-input problem has been improved by one comparator.
Target text information: Rapidly reconfigurable field-programmable gate arrays for accelerating fitness evaluation in genetic programming. : The dominant component of the computational burden of solving n o n trivial p r o b l e m s w i t h evolutionary algorithms is the task of measuring the fitness of each individual in each generation of the evolving population. The advent of r a p i d l y r e c o n f i g u r a b l e f i e l d - programmable gate arrays (FPGAs) and the idea of evolvable hardware opens the possiblity of e m b o d y i n g each individual of the evolving population into hardware for the purpose of accelerating the time-consuming fitness evaluation task This paper demonstrates how the massive parallelism of the rapidly r e c o n f i g u r a b l e X i l i n x X C 6 2 1 6 FPGA can be exploited to accelerate the computationally burdensome fitness evaluation task of genetic programming. The work was done on Virtual Computing Corporation's low-cost HOTS expansion board for PC type computers. A 16-step 7-sorter was evolved that has two fewer steps than the sorting network described in the 1962 O'Connor and Nelson patent on sorting networks and that has the same number of steps as the minimal 7-sorter that was devised by Floyd and Knuth subsequent to the patent.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Genetic Algorithms
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1-hop neighbor's text information: Finding structure in reinforcement learning. : Reinforcement learning addresses the problem of learning to select actions in order to maximize one's performance in unknown environments. To scale reinforcement learning to complex real-world tasks, such as typically studied in AI, one must ultimately be able to discover the structure in the world, in order to abstract away the myriad of details and to operate in more tractable problem spaces. This paper presents the SKILLS algorithm. SKILLS discovers skills, which are partially defined action policies that arise in the context of multiple, related tasks. Skills collapse whole action sequences into single operators. They are learned by minimizing the compactness of action policies, using a description length argument on their representation. Empirical results in simple grid navigation tasks illustrate the successful discovery of structure in reinforcement learning.
1-hop neighbor's text information: Discovering Structure in Multiple Learning Tasks: The TC Algorithm. : Recently, there has been an increased interest in lifelong machine learning methods, that transfer knowledge across multiple learning tasks. Such methods have repeatedly been found to outperform conventional, single-task learning algorithms when the learning tasks are appropriately related. To increase robustness of such approaches, methods are desirable that can reason about the relatedness of individual learning tasks, in order to avoid the danger arising from tasks that are unrelated and thus potentially misleading. This paper describes the task-clustering (TC) algorithm. TC clusters learning tasks into classes of mutually related tasks. When facing a new learning task, TC first determines the most related task cluster, then exploits information selectively from this task cluster only. An empirical study carried out in a mobile robot domain shows that TC outperforms its non-selective counterpart in situations where only a small number of tasks is relevant.
Target text information: The Role of Transfer in Learning (extended abstract):
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: A comparative utility analysis of case-based reasoning and control-rule learning systems. : The utility problem in learning systems occurs when knowledge learned in an attempt to improve a system's performance degrades performance instead. We present a methodology for the analysis of utility problems which uses computational models of problem solving systems to isolate the root causes of a utility problem, to detect the threshold conditions under which the problem will arise, and to design strategies to eliminate it. We present models of case-based reasoning and control-rule learning systems and compare their performance with respect to the swamping utility problem. Our analysis suggests that case-based reasoning systems are more resistant to the utility problem than control-rule learning systems. 1
1-hop neighbor's text information: Case-Based Planning to Learn: Learning can be viewed as a problem of planning a series of modifications to memory. We adopt this view of learning and propose the applicability of the case-based planning methodology to the task of planning to learn. We argue that relatively simple, fine-grained primitive inferential operators are needed to support flexible planning. We show that it is possible to obtain the benefits of case-based reasoning within a planning to learn framework.
1-hop neighbor's text information: A theory of questions and question asking. :
Target text information: The Use of Explicit Goals for Knowledge to Guide Inference and Learning. : Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infinite), the inferential resources available to any reasoning system are limited. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference. Not all inferences are equally useful to a given reasoning system. Any reasoning system that has goals (or any form of a utility function) and acts based on its beliefs indirectly assigns utility to its beliefs. Given limits on the process of inference, and variation in the utility of inferences, it is clear that a reasoner ought to draw the inferences that will be most valuable to it. This paper presents an approach to this problem that makes the utility of a (potential) belief an explicit part of the inference process. The method is to generate explicit desires for knowledge. The question of focus of attention is thereby transformed into two related problems: How can explicit desires for knowledge be used to control inference and facilitate resource-constrained goal pursuit in general? and, Where do these desires for knowledge come from? We present a theory of knowledge goals, or desires for knowledge, and their use in the processes of understanding and learning. The theory is illustrated using two case studies, a natural language understanding program that learns by reading novel or unusual newspaper stories, and a differential diagnosis program that improves its accuracy with experience.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: PUSH-PULL SHUNTING MODEL OF GANGLION CELLS Simulations of X and Y retinal ganglion cell behavior:
Target text information: Toward a unified theory of spatiotemporal processing in the retina. :
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: A Study on the Generalization Capabilities of XCS. : We analyze the generalization behavior of the XCS classifier system in environments in which only a few generalizations can be done. Experimental results presented in the paper evidence that the generalization mechanism of XCS can prevent it from learning even simple tasks in such environments. We present a new operator, named Specify, which contributes to the solution of this problem. XCS with the Specify operator, named XCSS, is compared to XCS in terms of performance and generalization capabilities in different types of environments. Experimental results show that XCSS can deal with a greater variety of environments and that it is more robust than XCS with respect to population size.
1-hop neighbor's text information: Evolving Optimal Populations with XCS Classifier Systems, :
1-hop neighbor's text information: Integrated Architectures for Learning, Planning and Reacting Based on Approximating Dynamic Programming, : This paper extends previous work with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming methods. Dyna architectures integrate trial-and-error (reinforcement) learning and execution-time planning into a single process operating alternately on the world and on a learned model of the world. In this paper, I present and show results for two Dyna architectures. The Dyna-PI architecture is based on dynamic programming's policy iteration method and can be related to existing AI ideas such as evaluation functions and universal plans (reactive systems). Using a navigation task, results are shown for a simple Dyna-PI system that simultaneously learns by trial and error, learns a world model, and plans optimal routes using the evolving world model. The Dyna-Q architecture is based on Watkins's Q-learning, a new kind of reinforcement learning. Dyna-Q uses a less familiar set of data structures than does Dyna-PI, but is arguably simpler to implement and use. We show that Dyna-Q architectures are easy to adapt for use in changing environments.
Target text information: Model of the Environment to Avoid Local Learning: Pier Luca Lanzi Technical Report N. 97.46 December 20 th , 1997
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Induction of multiscale temporal structure. : Learning structure in temporally-extended sequences is a difficult computational problem because only a fraction of the relevant information is available at any instant. Although variants of back propagation can in principle be used to find structure in sequences, in practice they are not sufficiently powerful to discover arbitrary contingencies, especially those spanning long temporal intervals or involving high order statistics. For example, in designing a connectionist network for music composition, we have encountered the problem that the net is able to learn musical structure that occurs locally in time|e.g., relations among notes within a musical phrase|but not structure that occurs over longer time periods|e.g., relations among phrases. To address this problem, we require a means of constructing a reduced description of the sequence that makes global aspects more explicit or more readily detectable. I propose to achieve this using hidden units that operate with different time constants. Simulation experiments indicate that slower time-scale hidden units are able to pick up global structure, structure that simply can not be learned by standard Many patterns in the world are intrinsically temporal, e.g., speech, music, the unfolding of events. Recurrent neural net architectures have been devised to accommodate time-varying sequences. For example, the architecture shown in Figure 1 can map a sequence of inputs to a sequence of outputs. Learning structure in temporally-extended sequences is a difficult computational problem because the input pattern may not contain all the task-relevant information at any instant. Thus, back propagation.
Target text information: Tau Net: A Neural Network for Modeling Temporal Variability: The ability to handle temporal variation is important when dealing with real-world dynamic signals. In many applications, inputs do not come in as fixed-rate sequences, but rather as signals with time scales that can vary from one instance to the next; thus, modeling dynamic signals requires not only the ability to recognize sequences but also the ability to handle temporal changes in the signal. This paper discusses "Tau Net," a neural network for modeling dynamic signals, and its application to speech. In Tau Net, sequence learning is accomplished using a combination of prediction, recurrence and time-delay connections. Temporal variability is modeled by having adaptable time constants in the network, which are adjusted with respect to the prediction error. Adapting the time constants changes the time scale of the network, and the adapted value of the network's time constant provides a measure of temporal variation in the signal. Tau Net has been applied to several simple signals: sets of sine waves differing in frequency and in phase [2], a multidimensional signal representing the walking gait of children [3], and the energy contour of a simple speech utterance [11]. Tau Net has also been shown to work on a voicing distinction task using synthetic speech data [12]. In this paper, Tau Net is applied to two speaker-independent tasks, vowel recognition (of f/ae/,/iy/,/ux/g) and consonant recognition (of f/p/,/t/,/k/g) using speech data taken from the TIMIT database. It is shown that Tau Nets, trained on medium-rate tokens, achieved about the same performance as networks without time constants trained on tokens at all rates, and performed better than networks without time constants trained on medium-rate tokens. Our results demonstrate Tau Net's ability to identify vowels and consonants at variable speech rates by extrapolating to rates not represented in the training set.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: Constructive similarity assessment: Using stored cases to define new situa tions. : A fundamental issue in case-based reasoning is similarity assessment: determining similarities and differences between new and retrieved cases. Many methods have been developed for comparing input case descriptions to the cases already in memory. However, the success of such methods depends on the input case description being sufficiently complete to reflect the important features of the new situation, which is not assured. In case-based explanation of anomalous events during story understanding, the anomaly arises because the current situation is incompletely understood; consequently, similarity assessment based on matches between known current features and old cases is likely to fail because of gaps in the current case's description. Our solution to the problem of gaps in a new case's description is an approach that we call constructive similarity assessment. Constructive similarity assessment treats similarity assessment not as a simple comparison between fixed new and old cases, but as a process for deciding which types of features should be investigated in the new situation and, if the features are borne out by other knowledge, added to the description of the current case. Constructive similarity assessment does not merely compare new cases to old: using prior cases as its guide, it dynamically carves augmented descriptions of new cases out of memory.
Target text information: Adaptive similarity assessment for case-based explanation. :
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Learning active classifiers. : Many classification algorithms are "passive", in that they assign a class-label to each instance based only on the description given, even if that description is incomplete. In contrast, an active classifier can | at some cost | obtain the values of missing attributes, before deciding upon a class label. The expected utility of using an active classifier depends on both the cost required to obtain the additional attribute values and the penalty incurred if it outputs the wrong classification. This paper considers the problem of learning near-optimal active classifiers, using a variant of the probably-approximately-correct (PAC) model. After defining the framework | which is perhaps the main contribution of this paper | we describe a situation where this task can be achieved efficiently, but then show that the task is often intractable.
1-hop neighbor's text information: Error-based and entropy-based discretization of continuous features. : We present a comparison of error-based and entropy-based methods for discretization of continuous features. Our study includes both an extensive empirical comparison as well as an analysis of scenarios where error minimization may be an inappropriate discretization criterion. We present a discretization method based on the C4.5 decision tree algorithm and compare it to an existing entropy-based discretization algorithm, which employs the Minimum Description Length Principle, and a recently proposed error-based technique. We evaluate these discretization methods with respect to C4.5 and Naive-Bayesian classifiers on datasets from the UCI repository and analyze the computational complexity of each method. Our results indicate that the entropy-based MDL heuristic outperforms error minimization on average. We then analyze the shortcomings of error-based approaches in comparison to entropy-based methods.
1-hop neighbor's text information: Mining for causes of cancer: Machine learning experiments at various levels of detail. : This paper presents, from a methodological point of view, first results of an interdisciplinary project in scientific data mining. We analyze data about the carcinogenicity of chemicals derived from the carcinogenesis bioassay program, a long-term research study performed by the US National Institute of Environmental Health Sciences. The database contains detailed descriptions of 6823 tests performed with more than 330 compounds and animals of different species, strains and sexes. The chemical structures are described at the atom and bond level, and in terms of various relevant structural properties. The goal of this paper is to investigate the effects that various levels of detail and amounts of information have on the resulting hypotheses, both quantitatively and qualitatively. We apply relational and propositional machine learning algorithms to learning problems formulated as regression or as classification tasks. In addition, these experiments have been conducted with two learning problems which are at different levels of detail. Quantitatively, our experiments indicate that additional information not necessarily improves accuracy. Qualitatively, a number of potential discoveries have been made by the algorithm for Relational Regression, because it is not forced to abstract from the details contained in the relations of the database.
Target text information: Theory and Applications of Agnostic PAC-Learning with Small Decision Trees, : We exhibit a theoretically founded algorithm T2 for agnostic PAC-learning of decision trees of at most 2 levels, whose computation time is almost linear in the size of the training set. We evaluate the performance of this learning algorithm T2 on 15 common real-world datasets, and show that for most of these datasets T2 provides simple decision trees with little or no loss in predictive power (compared with C4.5). In fact, for datasets with continuous attributes its error rate tends to be lower than that of C4.5. To the best of our knowledge this is the first time that a PAC-learning algorithm is shown to be applicable to real-world classification problems. Since one can prove that T2 is an agnostic PAC-learning algorithm, T2 is guaranteed to produce close to optimal 2-level decision trees from sufficiently large training sets for any (!) distribution of data. In this regard T2 differs strongly from all other learning algorithms that are considered in applied machine learning, for which no guarantee can be given about their performance on new datasets. We also demonstrate that this algorithm T2 can be used as a diagnostic tool for the investigation of the expressive limits of 2-level decision trees. Finally, T2, in combination with new bounds on the VC-dimension of decision trees of bounded depth that we derive, provides us now for the first time with the tools necessary for comparing learning curves of decision trees for real-world datasets with the theoretical estimates of PAC learning theory.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Priority ASOCS. : This paper presents an ASOCS (Adaptive Self-Organizing Concurrent System) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. An ASOCS can operate in either a data processing mode or a learning mode. During data processing mode, an ASOCS acts as a parallel hardware circuit. During learning mode, an ASOCS incorporates a rule expressed as a Boolean conjunction in a distributed fashion in time logarithmic in the number of rules. This paper proposes a learning algorithm and architecture for Priority ASOCS. This new ASOCS model uses rules with priorities. The new model has significant learning time and space complexity improvements over previous models. Non-von Neumann architectures such as neural networks attack the word-at-a-time bottleneck of traditional computing systems [1]. Neural networks learn input-output mappings using highly distributed processing and memory [10,11,12]. Their numerous simple processing elements with modifiable weighted links permit a high degree of parallelism. A typical neural network has fixed topology. It learns by modifying weighted links between nodes. A new class of connectionist architectures has been proposed called ASOCS (Adaptive Self-Organizing Concurrent Systems) [4,5]. ASOCS models support efficient computation through self-organized learning and parallel execution. Learning is done through the incremental presentation of rules and/or examples. ASOCS models learn by modifying their topology. Data types include Boolean and multi-state variables; recent models support analog variables. The model incorporates rules into an adaptive logic network in a parallel and self organizing fashion. In processing mode, ASOCS supports fully parallel execution on actual inputs according to the learned rules. The adaptive logic network acts as a parallel hardware circuit during execution, mapping n input boolean vectors into m output boolean vectors, in a combinatoric fashion. The overall philosophy of ASOCS follows the high level goals of current neural network models. However, the mechanisms of learning and execution vary significantly. The ASOCS logic network is topologically dynamic with the network growing to efficiently fit the specific application. Current ASOCS models are based on digital nodes. ASOCS also supports use of symbolic and heuristic learning mechanisms, thus combining the parallelism and distributed nature of connectionist computing with the potential power of AI symbolic learning. A proof of concept ASOCS chip has been developed [2].
1-hop neighbor's text information: A self-adjusting dynamic logic module. : This paper presents an ASOCS (Adaptive Self-Organizing Concurrent System) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on Adaptive Algorithm 2 (AA2) and details its architecture and learning algorithm. AA2 has significant memory and knowledge maintenance advantages over previous ASOCS models. An ASOCS can operate in either a data processing mode or a learning mode. During learning mode, the ASOCS is given a new rule expressed as a boolean conjunction. The AA2 learning algorithm incorporates the new rule in a distributed fashion in a short, bounded time. During data processing mode, the ASOCS acts as a parallel hardware circuit.
1-hop neighbor's text information: A self-organizing binary decision tree for incrementally defined rule based systems. : This paper presents an ASOCS (adaptive self-organizing concurrent system) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on adaptive algorithm 3 (AA3) and details its architecture and learning algorithm. It has advantages over previous ASOCS models in simplicity, implementability, and cost. An ASOCS can operate in either a data processing mode or a learning mode. During the data processing mode, an ASOCS acts as a parallel hardware circuit. In learning mode, rules expressed as boolean conjunctions are incrementally presented to the ASOCS. All ASOCS learning algorithms incorporate a new rule in a distributed fashion in a short, bounded time.
Target text information: Analysis of the Convergence and Generalization of AA1: AA1 is an incremental learning algorithm for Adaptive Self-Organizing Concurrent Systems (ASOCS). ASOCS are self-organizing, dynamically growing networks of computing nodes. AA1 learns by discrimination and implements knowledge in a distributed fashion over all the nodes. This paper reviews AA1 from the perspective of convergence and generalization. A formal proof that AA1 converges on any arbitrary Boolean instance set is given. A discussion of generalization and other aspects of AA1, including the problem of handling inconsistency, follows. Results of simulations with real-world data are presented. They show that AA1 gives promising generalization.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: Robust analysis of bayesian networks with finitely generated convex sets of distributions. : This paper presents exact solutions and convergent approximations for inferences in Bayesian networks associated with finitely generated convex sets of distributions. Robust Bayesian inference is the calculation of bounds on posterior values given perturbations in a probabilistic model. The paper presents exact inference algorithms and analyzes the circumstances where exact inference becomes intractable. Two classes of algorithms for numeric approximations are developed through transformations on the original model. The first transformation reduces the robust inference problem to the estimation of probabilistic parameters in a Bayesian network. The second transformation uses Lavine's bracketing algorithm to generate a sequence of maximization problems in a Bayesian network. The analysis is extended to the *-contaminated, the lower density bounded, the belief function, the sub-sigma, the density bounded, the total variation and the density ratio classes of distributions. c fl1996 Carnegie Mellon University
1-hop neighbor's text information: Generalized queries in probabilistic context-free grammars. : Probabilistic context-free grammars (PCFGs) provide a simple way to represent a particular class of distributions over sentences in a context-free language. Efficient parsing algorithms for answering particular queries about a PCFG (i.e., calculating the probability of a given sentence, or finding the most likely parse) have been developed, and applied to a variety of pattern-recognition problems. We extend the class of queries that can be answered in several ways: (1) allowing missing tokens in a sentence or sentence fragment, (2) supporting queries about intermediate structure, such as the presence of particular nonterminals, and (3) flexible conditioning on a variety of types of evidence. Our method works by constructing a Bayesian network to represent the distribution of parse trees induced by a given PCFG. The network structure mirrors that of the chart in a standard parser, and is generated using a similar dynamic-programming approach. We present an algorithm for constructing Bayesian networks from PCFGs, and show how queries or patterns of queries on the network correspond to interesting queries on PCFGs. The network formalism also supports extensions to encode various context sensitivities within the probabilistic dependency structure.
1-hop neighbor's text information: "Topological parameters for time-space tradeoff," : In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and deterministic networks as well as for accomplishing optimization tasks. By analyzing the problem structure it will be possible to select from a spectrum the algorithm that best meets a given time-space specifica tion.
Target text information: "Bucket elimination: A unifying framework for probabilistic inference," : Probabilistic inference algorithms for finding the most probable explanation, the maximum aposteriori hypothesis, and the maximum expected utility and for updating belief are reformulated as an elimination-type algorithm called bucket elimination. This emphasizes the principle common to many of the algorithms appearing in that literature and clarifies their relationship to nonserial dynamic programming algorithms. We also present a general way of combining conditioning and elimination within this framework. Bounds on complexity are given for all the algorithms as a function of the problem's struc ture.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Optimal mutation rates in genetic search. : The optimization of a single bit string by means of iterated mutation and selection of the best (a (1+1)-Genetic Algorithm) is discussed with respect to three simple fitness functions: The counting ones problem, a standard binary encoded integer, and a Gray coded integer optimization problem. A mutation rate schedule that is optimal with respect to the success probability of mutation is presented for each of the objective functions, and it turns out that the standard binary code can hamper the search process even in case of unimodal objective functions. While normally a mutation rate of 1=l (where l denotes the bit string length) is recommendable, our results indicate that a variation of the mutation rate is useful in cases where the fitness function is a multimodal pseudo-boolean function, where multimodality may be caused by the objective function as well as the encoding mechanism.
1-hop neighbor's text information: Mutation rates as adaptations. : In order to better understand life, it is helpful to look beyond the envelop of life as we know it. A simple model of coevolution was implemented with the addition of a gene for the mutation rate of the individual. This allowed the mutation rate itself to evolve in a lineage. The model shows that when the individuals interact in a sort of zero-sum game, the lineages maintain relatively high mutation rates. However, when individuals engage in interactions that have greater consequences for one individual in the interaction than the other, lineages tend to evolve relatively low mutation rates. This model suggests that different genes may have evolved different mutation rates as adaptations to the varying pressures of interactions with other genes.
Target text information: Between-host evolution of mutation-rate and within-host evolution of virulence.: It has been recently realized that parasite virulence (the harm caused by parasites to their hosts) can be an adaptive trait. Selection for a particular level of virulence can happen either at at the level of between-host tradeoffs or as a result of short-sighted within-host competition. This paper describes some simulations which study the effect that modifier genes for changes in mutation rate have on suppressing this short-sighted development of virulence, and investigates the interaction between this and a simplified model of im mune clearance.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Genetic Algorithms
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1-hop neighbor's text information: Genetic Algorithms in Search, Optimization and Machine Learning. : Angeline, P., Saunders, G. and Pollack, J. (1993) An evolutionary algorithm that constructs recurrent neural networks, LAIR Technical Report #93-PA-GNARLY, Submitted to IEEE Transactions on Neural Networks Special Issue on Evolutionary Programming.
Target text information: A Classifier System plays a simple board game Getting down to the Basics of Machine Learning?:
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: The BATmobile: Towards a Bayesian automated taxi. : The problem of driving an autonomous vehicle in normal traffic engages many areas of AI research and has substantial economic significance. We describe a new approach to this problem based on a decision-theoretic architecture using dynamic probabilistic networks. The architecture provides a sound solution to the problems of sensor noise, sensor failure, and uncertainty about the behavior of other vehicles and about the effects of one's own actions. We report on several advances in the theory and practice of inference and decision making in dynamic, partially observable domains. Our approach has been implemented in a simulation system, and the autonomous vehicle successfully negotiates a variety of difficult situations. Multiple submissions: This paper has not already been accepted by and is not currently under review for a journal or another conference. Nor will it be submitted for such during IJCAI's review period.
1-hop neighbor's text information: Generalized queries in probabilistic context-free grammars. : Probabilistic context-free grammars (PCFGs) provide a simple way to represent a particular class of distributions over sentences in a context-free language. Efficient parsing algorithms for answering particular queries about a PCFG (i.e., calculating the probability of a given sentence, or finding the most likely parse) have been developed, and applied to a variety of pattern-recognition problems. We extend the class of queries that can be answered in several ways: (1) allowing missing tokens in a sentence or sentence fragment, (2) supporting queries about intermediate structure, such as the presence of particular nonterminals, and (3) flexible conditioning on a variety of types of evidence. Our method works by constructing a Bayesian network to represent the distribution of parse trees induced by a given PCFG. The network structure mirrors that of the chart in a standard parser, and is generated using a similar dynamic-programming approach. We present an algorithm for constructing Bayesian networks from PCFGs, and show how queries or patterns of queries on the network correspond to interesting queries on PCFGs. The network formalism also supports extensions to encode various context sensitivities within the probabilistic dependency structure.
1-hop neighbor's text information: Sonderforschungsbereich 314 K unstliche Intelligenz Wissensbasierte Systeme KI-Labor am Lehrstuhl f ur Informatik IV Numerical:
Target text information: Accounting for context in plan recognition, with application to traffic monitoring. : Typical approaches to plan recognition start from a representation of an agent's possible plans, and reason evidentially from observations of the agent's actions to assess the plausibility of the various candidates. A more expansive view of the task (consistent with some prior work) accounts for the context in which the plan was generated, the mental state and planning process of the agent, and consequences of the agent's actions in the world. We present a general Bayesian framework encompassing this view, and focus on how context can be exploited in plan recognition. We demonstrate the approach on a problem in traffic monitoring, where the objective is to induce the plan of the driver from observation of vehicle movements. Starting from a model of how the driver generates plans, we show how the highway context can appropriately influence the recognizer's interpretation of observed driver be havior.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Self-organized formation of typologically correct feature maps. : 2] D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning Internal Representations by Error Propagation", in D. E. Rumelhart and J. L. McClelland (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1), MIT Press (1986).
1-hop neighbor's text information: Growing Cell Structures A Self-Organizing Network for Unsupervised and Supervised Learning, : We present a new self-organizing neural network model having two variants. The first variant performs unsupervised learning and can be used for data visualization, clustering, and vector quantization. The main advantage over existing approaches, e.g., the Kohonen feature map, is the ability of the model to automatically find a suitable network structure and size. This is achieved through a controlled growth process which also includes occasional removal of units. The second variant of the model is a supervised learning method which results from the combination of the abovementioned self-organizing network with the radial basis function (RBF) approach. In this model it is possible in contrast to earlier approaches toperform the positioning of the RBF units and the supervised training of the weights in parallel. Therefore, the current classification error can be used to determine where to insert new RBF units. This leads to small networks which generalize very well. Results on the two-spirals benchmark and a vowel classification problem are presented which are better than any results previously published. fl submitted for publication
1-hop neighbor's text information: Some Competitive Learning Methods (Some additions and refinements are planned for:
Target text information: "The LBG-U method for vector quantization An improvement over LBG inspired from neural networks," : Internal Report 97-01
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: Bias, variance and prediction error for classification rules. : We study the notions of bias and variance for classification rules. Following Efron (1978) we develop a decomposition of prediction error into its natural components. Then we derive bootstrap estimates of these components and illustrate how they can be used to describe the error behaviour of a classifier in practice. In the process we also obtain a bootstrap estimate of the error of a "bagged" classifier.
1-hop neighbor's text information: On learning hierarchical classifications: Many significant real-world classification tasks involve a large number of categories which are arranged in a hierarchical structure; for example, classifying documents into subject categories under the library of congress scheme, or classifying world-wide-web documents into topic hierarchies. We investigate the potential benefits of using a given hierarchy over base classes to learn accurate multi-category classifiers for these domains. First, we consider the possibility of exploiting a class hierarchy as prior knowledge that can help one learn a more accurate classifier. We explore the benefits of learning category-discriminants in a hard top-down fashion and compare this to a soft approach which shares training data among sibling categories. In doing so, we verify that hierarchies have the potential to improve prediction accuracy. But we argue that the reasons for this can be subtle. Sometimes, the improvement is only because using a hierarchy happens to constrain the expressiveness of a hypothesis class in an appropriate manner. However, various controlled experiments show that in other cases the performance advantage associated with using a hierarchy really does seem to be due to the prior knowledge it encodes.
1-hop neighbor's text information: MAJORITY VOTE CLASSIFIERS: THEORY AND APPLICATIONS:
Target text information: Bias plus variance decomposition for zero-one loss functions. : We present a bias-variance decomposition of expected misclassification rate, the most commonly used loss function in supervised classification learning. The bias-variance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms, yet no decomposition was offered for the more commonly used zero-one (misclassification) loss functions until the recent work of Kong & Dietterich (1995) and Breiman (1996). Their decomposition suffers from some major shortcomings though (e.g., potentially negative variance), which our decomposition avoids. We show that, in practice, the naive frequency-based estimation of the decomposition terms is by itself biased and show how to correct for this bias. We illustrate the decomposition on various algorithms and datasets from the UCI repository.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Theory
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1-hop neighbor's text information: Genetic Algorithms in Search, Optimization and Machine Learning. : Angeline, P., Saunders, G. and Pollack, J. (1993) An evolutionary algorithm that constructs recurrent neural networks, LAIR Technical Report #93-PA-GNARLY, Submitted to IEEE Transactions on Neural Networks Special Issue on Evolutionary Programming.
1-hop neighbor's text information: "Using case based learning to improve genetic algorithm based design optimization", : In this paper we describe a method for improving genetic-algorithm-based optimization using case-based learning. The idea is to utilize the sequence of points explored during a search to guide further exploration. The proposed method is particularly suitable for continuous spaces with expensive evaluation functions, such as arise in engineering design. Empirical results in two engineering design domains and across different representations demonstrate that the proposed method can significantly improve the efficiency and reliability of the GA optimizer. Moreover, the results suggest that the modification makes the genetic algorithm less sensitive to poor choices of tuning parameters such as muta tion rate.
1-hop neighbor's text information: Adaptation of genetic algorithms for engineering design optimization. : Genetic algorithms have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains it was observed that a simple classical implementation of the GA based on binary encoding and bit mutation and crossover was sometimes inefficient and unable to reach the global optimum. Using floating point representation alone does not eliminate the problem. In this paper we describe a way of augmenting the GA with new operators and strategies that take advantage of the structure and properties of such engineering design domains. Empirical results (initially in the domain of conceptual design of supersonic transport aircraft and the domain of high performance supersonic missile inlet design) demonstrate that the newly formulated GA can be significantly better than the classical GA in terms of efficiency and reliability. http://www.cs.rutgers.edu/~shehata/papers.html
Target text information: Guided crossover: A new operator for genetic algorithm based optimization. : Genetic algorithms (GAs) have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. They have a much better chance of getting to global optima than gradient based methods which usually converge to local sub optima. However, GAs have a tendency of getting only moderately close to the optima in a small number of iterations. To get very close to the optima, the GA needs a very large number of iterations. Whereas gradient based optimizers usually get very close to local optima in a relatively small number of iterations. In this paper we describe a new crossover operator which is designed to endow the GA with gradient-like abilities without actually computing any gradients and without sacrificing global optimality. The operator works by using guidance from all members of the GA population to select a direction for exploration. Empirical results in two engineering design domains and across both binary and floating point representations demonstrate that the operator can significantly improve the steady state error of the GA optimizer.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Genetic Algorithms
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1-hop neighbor's text information: Hidden Markov models in computational biology: Applications to protein modeling. : Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated on the globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the SWISS-PROT 22 database for other sequences that are members of the given protein family, or contain the given domain. The HMM produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate three-dimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EF-hand HMMs), the HMM is able to distinguish members of these families from non-members with a high degree of accuracy. Both the HMM and PRO-FILESEARCH (a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appears to have a slight advantage
Target text information: A gentle guide to multiple alignment 2.03. : Prerequisites. An understanding of the dynamic programming (edit distance) approach to pairwise sequence alignment is useful for parts 1.3, 1.4, and 2. Also, familiarity with the use of Internet resources would be helpful for part 3. For the former, see Chapters 1.1 - 1.3, and for the latter, see Chapter 2 of the Hypertext Book of the GNA-VSNS Biocomputing Course at http://www.techfak.uni-bielefeld.de/bcd/Curric/welcome.html. General Rationale. You will understand why Multiple Alignment is considered a challenging problem, you will study approaches that try to reduce the number of steps needed to calculate the optimal solution, and you will study fast heuristics. In a case study involving immunoglobulin sequences, you will study multiple alignments obtained from WWW servers, recapitulating results from an original paper. Revision History. Version 1.01 on 17 Sep 1995. Expanded Ex.9. Updated Ex.46. Revised Solution Sheet -re- Ex.3+12. Marked more Exercises by "A" (to be submitted to the Instructor). Various minor clarifications in content
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: Malicious Membership Queries and Exceptions:
1-hop neighbor's text information: Learning Boolean read-once formulas with arbitrary symmetric and constant fan-in gates. : A read-once formula is a boolean formula in which each variable occurs at most once. Such formulas are also called -formulas or boolean trees. This paper treats the problem of exactly identifying an unknown read-once formula using specific kinds of queries. The main results are a polynomial time algorithm for exact identification of monotone read-once formulas using only membership queries, and a polynomial time algorithm for exact identification of general read-once formulas using equivalence and membership queries (a protocol based on the notion of a minimally adequate teacher [1]). Our results improve on Valiant's previous results for read-once formulas [26]. We also show that no polynomial time algorithm using only membership queries or only equivalence queries can exactly identify all read-once formulas.
Target text information: Exact learning of -DNF formulas with malicious membership queries. :
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Theory
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1-hop neighbor's text information: Incremental coevolution of organisms: A new approach for optimization and discovery of strategies. : In the field of optimization and machine learning techniques, some very efficient and promising tools like Genetic Algorithms (GAs) and Hill-Climbing have been designed. In this same field, the Evolving Non-Determinism (END) model presented in this paper proposes an inventive way to explore the space of states that, using the simulated "incremental" co-evolution of some organisms, remedies some drawbacks of these previous techniques and even allow this model to outperform them on some difficult problems. This new model has been applied to the sorting network problem, a reference problem that challenged many computer scientists, and an original one-player game named Solitaire. For the first problem, the END model has been able to build from "scratch" some sorting networks as good as the best known for the 16-input problem. It even improved by one comparator a 25 years old result for the 13-input problem. For the Solitaire game, END evolved a strategy comparable to a human designed strategy.
1-hop neighbor's text information: Dynamic control of genetic algorithms using fuzzy logic techniques. : This paper proposes using fuzzy logic techniques to dynamically control parameter settings of genetic algorithms (GAs). We describe the Dynamic Parametric GA: a GA that uses a fuzzy knowledge-based system to control GA parameters. We then introduce a technique for automatically designing and tuning the fuzzy knowledge-base system using GAs. Results from initial experiments show a performance improvement over a simple static GA. One Dynamic Parametric GA system designed by our automatic method demonstrated improvement on an application not included in the design phase, which may indicate the general applicability of the Dynamic Parametric GA to a wide range of ap plications.
1-hop neighbor's text information: On The State of Evolutionary Computation: In the past few years the evolutionary computation landscape has been rapidly changing as a result of increased levels of interaction between various research groups and the injection of new ideas which challenge old tenets. The effect has been simultaneously exciting, invigorating, annoying, and bewildering to the old-timers as well as the new-comers to the field. Emerging out of all of this activity are the beginnings of some structure, some common themes, and some agreement on important open issues. We attempt to summarize these emergent properties in this paper.
Target text information: Dynamic parameter encoding for Genetic Algorithms. : The common use of static binary place-value codes for real-valued parameters of the phenotype in Holland's genetic algorithm (GA) forces either the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding (DPE) is a mechanism that avoids this dilemma by using convergence statistics derived from the GA population to adaptively control the mapping from fixed-length binary genes to real values. DPE is shown to be empirically effective and amenable to analysis; we explore the problem of premature convergence in GAs through two convergence models.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Genetic Algorithms
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cora
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test
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1-hop neighbor's text information: A fixed size storage O(n 3 ) time complexity learning algorithm for fully recurrent continually running networks. : The RTRL algorithm for fully recurrent continually running networks (Robinson and Fallside, 1987)(Williams and Zipser, 1989) requires O(n 4 ) computations per time step, where n is the number of non-input units. I describe a method suited for on-line learning which computes exactly the same gradient and requires fixed-size storage of the same order but has an average time complexity 1 per time step of O(n 3 ).
1-hop neighbor's text information: Predicting sunspots and exchange rates with connectionist networks. : We investigate the effectiveness of connectionist networks for predicting the future continuation of temporal sequences. The problem of overfitting, particularly serious for short records of noisy data, is addressed by the method of weight-elimination: a term penalizing network complexity is added to the usual cost function in back-propagation. The ultimate goal is prediction accuracy. We analyze two time series. On the benchmark sunspot series, the networks outperform traditional statistical approaches. We show that the network performance does not deteriorate when there are more input units than needed. Weight-elimination also manages to extract some part of the dynamics of the notoriously noisy currency exchange rates and makes the network solution interpretable.
1-hop neighbor's text information: Resonance and the perception of musical meter. : Many connectionist approaches to musical expectancy and music composition let the question of What next? overshadow the equally important question of When next?. One cannot escape the latter question, one of temporal structure, when considering the perception of musical meter. We view the perception of metrical structure as a dynamic process where the temporal organization of external musical events synchronizes, or entrains, a listeners internal processing mechanisms. This article introduces a novel connectionist unit, based upon a mathematical model of entrainment, capable of phase and frequency-locking to periodic components of incoming rhythmic patterns. Networks of these units can self-organize temporally structured responses to rhythmic patterns. The resulting network behavior embodies the perception of metrical structure. The article concludes with a discussion of the implications of our approach for theories of metrical structure and musical expectancy.
Target text information: M.C., "Neural Net Architectures for Temporal Sequence Processing," Predicting the future and understanding the past (Eds. : I present a general taxonomy of neural net architectures for processing time-varying patterns. This taxonomy subsumes many existing architectures in the literature, and points to several promising architectures that have yet to be examined. Any architecture that processes time-varying patterns requires two conceptually distinct components: a short-term memory that holds on to relevant past events and an associator that uses the short-term memory to classify or predict. My taxonomy is based on a characterization of short-term memory models along the dimensions of form, content, and adaptability. Experiments on predicting future values of a financial time series (US dollar-Swiss franc exchange rates) are presented using several alternative memory models. The results of these experiments serve as a baseline against which more sophisticated architectures can be compared. Neural networks have proven to be a promising alternative to traditional techniques for nonlinear temporal prediction tasks (e.g., Curtiss, Brandemuehl, & Kreider, 1992; Lapedes & Farber, 1987; Weigend, Huberman, & Rumelhart, 1992). However, temporal prediction is a particularly challenging problem because conventional neural net architectures and algorithms are not well suited for patterns that vary over time. The prototypical use of neural nets is in structural pattern recognition. In such a task, a collection of features|visual, semantic, or otherwise|is presented to a network and the network must categorize the input feature pattern as belonging to one or more classes. For example, a network might be trained to classify animal species based on a set of attributes describing living creatures such as "has tail", "lives in water", or "is carnivorous"; or a network could be trained to recognize visual patterns over a two-dimensional pixel array as a letter in fA; B; . . . ; Zg. In such tasks, the network is presented with all relevant information simultaneously. In contrast, temporal pattern recognition involves processing of patterns that evolve over time. The appropriate response at a particular point in time depends not only on the current input, but potentially all previous inputs. This is illustrated in Figure 1, which shows the basic framework for a temporal prediction problem. I assume that time is quantized into discrete steps, a sensible assumption because many time series of interest are intrinsically discrete, and continuous series can be sampled at a fixed interval. The input at time t is denoted x(t). For univariate series, this input
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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cora
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1-hop neighbor's text information: Efficient distribution-free learning of probabilistic concepts. : In this paper we investigate a new formal model of machine learning in which the concept (boolean function) to be learned may exhibit uncertain or probabilistic behavior|thus, the same input may sometimes be classified as a positive example and sometimes as a negative example. Such probabilistic concepts (or p-concepts) may arise in situations such as weather prediction, where the measured variables and their accuracy are insufficient to determine the outcome with certainty. We adopt from the Valiant model of learning [27] the demands that learning algorithms be efficient and general in the sense that they perform well for a wide class of p-concepts and for any distribution over the domain. In addition to giving many efficient algorithms for learning natural classes of p-concepts, we study and develop in detail an underlying theory of learning p-concepts.
1-hop neighbor's text information: "A General Lower Bound on the Number of Examples Needed for Learning," : We prove a lower bound of ( 1 * ln 1 ffi + VCdim(C) * ) on the number of random examples required for distribution-free learning of a concept class C, where VCdim(C) is the Vapnik-Chervonenkis dimension and * and ffi are the accuracy and confidence parameters. This improves the previous best lower bound of ( 1 * ln 1 ffi + VCdim(C)), and comes close to the known general upper bound of O( 1 ffi + VCdim(C) * ln 1 * ) for consistent algorithms. We show that for many interesting concept classes, including kCNF and kDNF, our bound is actually tight to within a constant factor.
1-hop neighbor's text information: A note on learning from multiple--instance examples. : We describe a simple reduction from the problem of PAC-learning from multiple-instance examples to that of PAC-learning with one-sided random classification noise. Thus, all concept classes learnable with one-sided noise, which includes all concepts learnable in the usual 2-sided random noise model plus others such as the parity function, are learnable from multiple-instance examples. We also describe a more efficient (and somewhat technically more involved) reduction to the Statistical-Query model that results in a polynomial-time algorithm for learning axis-parallel rectangles with sample complexity ~ O(d 2 r=* 2 ), saving roughly a factor of r over the results of Auer et al. (1997).
Target text information: PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples. : We describe a polynomial-time algorithm for learning axis-aligned rectangles in Q d with respect to product distributions from multiple-instance examples in the PAC model. Here, each example consists of n elements of Q d together with a label indicating whether any of the n points is in the rectangle to be learned. We assume that there is an unknown product distribution D over Q d such that all instances are independently drawn according to D. The accuracy of a hypothesis is measured by the probability that it would incorrectly predict whether one of n more points drawn from D was in the rectangle to be learned. Our algorithm achieves accuracy * with probability 1 ffi in
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Theory
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1-hop neighbor's text information: Learning from examples, agent teams and the concept of reflection. : In International Journal of Pattern Recognition and AI, 10(3):251-272, 1996 Also available as GMD report #766
1-hop neighbor's text information: Multiple network systems (MINOS) modules: Task division and module discrimination. : It is widely considered an ultimate connectionist objective to incorporate neural networks into intelligent systems. These systems are intended to possess a varied repertoire of functions enabling adaptable interaction with a non-static environment. The first step in this direction is to develop various neural network algorithms and models, the second step is to combine such networks into a modular structure that might be incorporated into a workable system. In this paper we consider one aspect of the second point, namely: processing reliability and hiding of wetware details. Pre- sented is an architecture for a type of neural expert module, named an Authority. An Authority consists of a number of Minos modules. Each of the Minos modules in an Authority has the same processing capabilities, but varies with respect to its particular specialization to aspects of the problem domain. The Authority employs the collection of Minoses like a panel of experts. The expert with the highest confidence is believed, and it is the answer and confidence quotient that are transmitted to other levels in a system hierarchy.
1-hop neighbor's text information: The Pandemonium system of reflective agents. : In IEEE Transactions on Neural Networks, 7(1):97-106, 1996 Also available as GMD report #794
Target text information: Data exploration with reflective adaptive models. :
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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cora
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1-hop neighbor's text information: Self-Organization and Associative Memory, : Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feed-forward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedfor-ward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response.
1-hop neighbor's text information: Neuronlike adaptive elements that can solve difficult learning control problems. : Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic neural networks. Technical Report FKI-129-90, Technische Universitat Munchen, Institut fu Informatik. Servan-Schreiber, D., Cleermans, A., and McClelland, J. (1988). Encoding sequential structure in simple recurrent networks. Technical Report CMU-CS-88-183, Carnegie Mellon University, Computer Science Department.
Target text information: and Deva Johnam. A neural network pole-balancer that learns and operates on a real robot in real time. : A neural network approach to the classic inverted pendulum task is presented. This task is the task of keeping a rigid pole, hinged to a cart and free to fall in a plane, in a roughly vertical orientation by moving the cart horizontally in the plane while keeping the cart within some maximum distance of its starting position. This task constitutes a difficult control problem if the parameters of the cart-pole system are not known precisely or are variable. It also forms the basis of an even more complex control-learning problem if the controller must learn the proper actions for successfully balancing the pole given only the current state of the system and a failure signal when the pole angle from the vertical becomes too great or the cart exceeds one of the boundaries placed on its position. The approach presented is demonstrated to be effective for the real-time control of a small, self-contained mini-robot, specially outfitted for the task. Origins and details of the learning scheme, specifics of the mini-robot hardware, and results of actual learning trials are presented.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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cora
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1-hop neighbor's text information: Topography and ocular dominance: A model exploring positive correla-tions. : The map from eye to brain in vertebrates is topographic, i.e. neighbouring points in the eye map to neighbouring points in the brain. In addition, when two eyes innervate the same target structure, the two sets of fibres segregate to form ocular dominance stripes. Experimental evidence from the frog and goldfish suggests that these two phenomena may be subserved by the same mechanisms. We present a computational model that addresses the formation of both topography and ocular dominance. The model is based on a form of competitive learning with subtractive enforcement of a weight normalization rule. Inputs to the model are distributed patterns of activity presented simultaneously in both eyes. An important aspect of this model is that ocular dominance segregation can occur when the two eyes are positively correlated, whereas previous models have tended to assume zero or negative correlations between the eyes. This allows investigation of the dependence of the pattern of stripes on the degree of correlation between the eyes: we find that increasing correlation leads to narrower stripes. Experiments are suggested to test this prediction.
1-hop neighbor's text information: Breaking Rotational Symmetry in a Self-Organizing Map-Model for Orientation Map Development:
Target text information: Analyzing phase transitions in high-dimensional self-organizing maps. : The Self-Organizing Map (SOM), a widely used algorithm for the unsupervised learning of neural maps, can be formulated in a low-dimensional "feature map" variant which requires prespecified parameters ("features") for the description of receptive fields, or in a more general high-dimensional variant which allows to self-organize the structure of individual receptive fields as well as their arrangement in a map. We present here a new analytical method to derive conditions for the emergence of structure in SOMs which is particularly suited for the as yet inaccessible high-dimensional SOM variant. Our approach is based on an evaluation of a map distortion function. It involves only an ansatz for the way stimuli are distributed among map neurons; the receptive fields of the map need not be known explicitely. Using this method we first calculate regions of stability for four possible states of SOMs projecting from a rectangular input space to a ring of neurons. We then analyze the transition from non-oriented to oriented receptive fields in a SOM-based model for the development of orientation maps. In both cases, the analytical results are well corroborated by the results of computer simulations. submitted to Biological Cybernetics, December 14, 1995 revised version, July 14, 1996
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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cora
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1-hop neighbor's text information: A model of similarity-based retrieval. : We present a model of similarity-based retrieval which attempts to capture three psychological phenomena: (1) people are extremely good at judging similarity and analogy when given items to compare. (2) Superficial remindings are much more frequent than structural remindings. (3) People sometimes experience and use purely structural analogical re-mindings. Our model, called MAC/FAC (for "many are called but few are chosen") consists of two stages. The first stage (MAC) uses a computationally cheap, non-structural matcher to filter candidates from a pool of memory items. That is, we redundantly encode structured representations as content vectors, whose dot product yields an estimate of how well the corresponding structural representations will match. The second stage (FAC) uses SME to compute a true structural match between the probe and output from the first stage. MAC/FAC has been fully implemented, and we show that it is capable of modeling patterns of access found in psychological data.
1-hop neighbor's text information: The Structure-Mapping Engine: Algorithms and Examples. : This paper describes the Structure-Mapping Engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's Structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well. We review the Structure-mapping theory and describe the design of the engine. We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial, typically bounded by O (N 2 ). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work. This paper appeared in Artificial Intelligence, 41, 1989, pp 1-63. For more information, please contact [email protected]
1-hop neighbor's text information: Structural evaluation of analogies: : Judgments of similarity and soundness are important aspects of human analogical processing. This paper explores how these judgments can be modeled using SME, a simulation of Gentner's structure-mapping theory. We focus on structural evaluation, explicating several principles which psychologically plausible algorithms should follow. We introduce the Specificity Conjecture, which claims that naturalistic representations include a preponderance of appearance and low-order information. We demonstrate via computational experiments that this conjecture affects how structural evaluation should be performed, including the choice of normalization technique and how the systematicity preference is implemented.
Target text information: Making SME greedy and pragmatic. : The Structure-Mapping Engine (SME) has successfully modeled several aspects of human consistent interpretations of an analogy. While useful for theoretical explorations, this aspect of the algorithm is both psychologically implausible and computationally inefficient. (2) SME contains no mechanism for focusing on interpretations relevant to an analogizer's goals. This paper describes modifications to SME which overcome these flaws. We describe a greedy merge algorithm which efficiently computes an approximate "best" interpretation, and can generate alternate interpretations when necessary. We describe pragmatic marking, a technique which focuses the mapping to produce relevant, yet novel, inferences. We illustrate these techniques via example and evaluate their performance using empirical data and theoretical analysis. analogical processing. However, it has two significant drawbacks: (1) SME constructs all structurally
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Case Based
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cora
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1-hop neighbor's text information: "Multistrategy Learning in Reactive Control Systems for Autonomous Robotic Navigation," : This paper presents a self-improving reactive control system for autonomous robotic navigation. The navigation module uses a schema-based reactive control system to perform the navigation task. The learning module combines case-based reasoning and reinforcement learning to continuously tune the navigation system through experience. The case-based reasoning component perceives and characterizes the system's environment, retrieves an appropriate case, and uses the recommendations of the case to tune the parameters of the reactive control system. The reinforcement learning component refines the content of the cases based on the current experience. Together, the learning components perform on-line adaptation, resulting in improved performance as the reactive control system tunes itself to the environment, as well as on-line learning, resulting in an improved library of cases that capture environmental regularities necessary to perform on-line adaptation. The system is extensively evaluated through simulation studies using several performance metrics and system configurations.
1-hop neighbor's text information: Using Case-Based Reasoning for Mobile Robot Navigation: This paper presents an approach to mobile robot path planning using case-based reasoning together with map-based path planning. The map-based path planner is used to seed the case-base with innovative solutions. The casebase stores the paths and the information about their traversability. While planning the route those paths are preferred that according to the former experience are least risky.
Target text information: A Case-based Approach to Reactive Control for Autonomous Robots. : We propose a case-based method of selecting behavior sets as an addition to traditional reactive robotic control systems. The new system (ACBARR | A Case BAsed Reactive Robotic system) provides more flexible performance in novel environments, as well as overcoming a standard "hard" problem for reactive systems, the box canyon. Additionally, ACBARR is designed in a manner which is intended to remain as close to pure reactive control as possible. Higher level reasoning and memory functions are intentionally kept to a minimum. As a result, the new reasoning does not significantly slow the system down from pure reactive speeds.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Case Based
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cora
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train
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1-hop neighbor's text information: The Structure-Mapping Engine: Algorithms and Examples. : This paper describes the Structure-Mapping Engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's Structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well. We review the Structure-mapping theory and describe the design of the engine. We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial, typically bounded by O (N 2 ). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work. This paper appeared in Artificial Intelligence, 41, 1989, pp 1-63. For more information, please contact [email protected]
Target text information: Representing physical and design knowledge in innovative engineering design. :
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Case Based
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cora
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1-hop neighbor's text information: Smoothing spline ANOVA for exponential families, with application to the Wisconsin Epidemiological Study of Diabetic Retinopathy. :
1-hop neighbor's text information: "Bootstrap Confidence Intervals for Smoothing Splines and Their Comparison to Bayesian Confidence Intervals". : Bayesian confidence intervals of a smoothing spline are often used to distinguish two curves. In this paper, we provide an asymptotic formula for sample size calculations based on Bayesian confidence intervals. Approximations and simulations on special functions indicate that this asymptotic formula is reasonably accurate. Key Words: Bayesian confidence intervals; sample size; smoothing spline. fl Address: Department of Statistics and Applied Probability, University of California, Santa Barbara, CA 93106-3110. Tel.: (805)893-4870. Fax: (805)893-2334. E-mail: [email protected]. Supported by the National Institute of Health under Grants R01 EY09946, P60 DK20572 and P30 HD18258.
1-hop neighbor's text information: Spatial-temporal analysis of temperature using smoothing spline ANOVA. :
Target text information: Backfitting in smoothing spline ANOVA with application to historical global temperature data (thesis). : A computational scheme for fitting smoothing spline ANOVA models to large data sets with a (near) tensor product design is proposed. Such data sets are common in spatial-temporal analyses. The proposed scheme uses the backfitting algorithm to take advantage of the tensor product design to save both computational memory and time. Several ways to further speed up the backfitting algorithm, such as collapsing component functions and successive over-relaxation, are discussed. An iterative imputation procedure is used to handle the cases of near tensor product designs. An application to a global historical surface air temperature data set, which motivated this work, is used to illustrate the scheme proposed.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Probabilistic Methods
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cora
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1-hop neighbor's text information: Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions. : We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities are automatically extracted form the knowledge base to facilitate the construction and retraction of plausible beliefs. To represent causation, the formalism incorporates the principle of Markov shielding which imposes a stratified set of independence constraints on rankings of interpretations. We show how this formalism resolves some classical problems associated with specificity, prediction and abduction, and how it offers a natural way of unifying belief revision, belief update, and reasoning about actions.
1-hop neighbor's text information: Plausibility measures and default reasoning. : In recent years, a number of different semantics for defaults have been proposed, such as preferential structures, *- semantics, possibilistic structures, and -rankings, that have been shown to be characterized by the same set of axioms, known as the KLM properties (for Kraus, Lehmann, and Magidor). While this was viewed as a surprise, we show here that it is almost inevitable. We do this by giving yet another semantics for defaults that uses plausibility measures, a new approach to modeling uncertainty that generalize other approaches, such as probability measures, belief functions, and possibility measures. We show that all the earlier approaches to default reasoning can be embedded in the framework of plausibility. We then provide a necessary and sufficient condition on plausibilities for the KLM properties to be sound, and an additional condition necessary and sufficient for the KLM properties to be complete. These conditions are easily seen to hold for all the earlier approaches, thus explaining why they are characterized by the KLM properties.
1-hop neighbor's text information: A qualitative Markov assumption and its implications for belief change. : The study of belief change has been an active area in philosophy and AI. In recent years, two special cases of belief change, belief revision and belief update, have been studied in detail. Roughly speaking, revision treats a surprising observation as a sign that previous beliefs were wrong, while update treats a surprising observation as an indication that the world has changed. In general, we would expect that an agent making an observation may both want to revise some earlier beliefs and assume that some change has occurred in the world. We define a novel approach to belief change that allows us to do this, by applying ideas from probability theory in a qualitative settings. The key idea is to use a qualitative Markov assumption, which says that state transitions are independent. We show that a recent approach to modeling qualitative uncertainty using plausibility measures allows us to make such a qualitative Markov assumption in a relatively straightforward way, and show how the Markov assumption can be used to provide an attractive belief-change model.
Target text information: Plausibility Measures: A User's Guide: We examine a new approach to modeling uncertainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially ordered set. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. The lack of structure in a plausibility measure makes it easy for us to add structure on an as needed basis, letting us examine what is required to ensure that a plausibility measure has certain properties of interest. This gives us insight into the essential features of the properties in question, while allowing us to prove general results that apply to many approaches to reasoning about uncertainty. Plausibility measures have already proved useful in analyzing default reasoning. In this paper, we examine their algebraic properties, analogues to the use of + and fi in probability theory. An understanding of such properties will be essential if plausibility measures are to be used in practice as a representation tool.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Probabilistic Methods
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cora
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1-hop neighbor's text information: Case-Based Planning to Learn: Learning can be viewed as a problem of planning a series of modifications to memory. We adopt this view of learning and propose the applicability of the case-based planning methodology to the task of planning to learn. We argue that relatively simple, fine-grained primitive inferential operators are needed to support flexible planning. We show that it is possible to obtain the benefits of case-based reasoning within a planning to learn framework.
1-hop neighbor's text information: Acquiring case adaptation knowledge: A hybrid approach. : The ability of case-based reasoning (CBR) systems to apply cases to novel situations depends on their case adaptation knowledge. However, endowing CBR systems with adequate adaptation knowledge has proven to be a very difficult task. This paper describes a hybrid method for performing case adaptation, using a combination of rule-based and case-based reasoning. It shows how this approach provides a framework for acquiring flexible adaptation knowledge from experiences with autonomous adaptation and suggests its potential as a basis for acquisition of adaptation knowledge from interactive user guidance. It also presents initial experimental results examining the benefits of the approach and comparing the relative contributions of case learning and adaptation learning to reasoning performance.
1-hop neighbor's text information: Linking adaptation and similarity learning. : In current CBR systems, case adaptation is usually performed by rule-based methods that use task-specific rules hand-coded by the system developer. The ability to define those rules depends on knowledge of the task and domain that may not be available a priori, presenting a serious impediment to endowing CBR systems with the needed adaptation knowledge. This paper describes ongoing research on a method to address this problem by acquiring adaptation knowledge from experience. The method uses reasoning from scratch, based on introspective reasoning about the requirements for successful adaptation, to build up a library of adaptation cases that are stored for future reuse. We describe the tenets of the approach and the types of knowledge it requires. We sketch initial computer implementation, lessons learned, and open questions for further study.
Target text information: Combining rules and cases to learn case adaptation. : Computer models of case-based reasoning (CBR) generally guide case adaptation using a fixed set of adaptation rules. A difficult practical problem is how to identify the knowledge required to guide adaptation for particular tasks. Likewise, an open issue for CBR as a cognitive model is how case adaptation knowledge is learned. We describe a new approach to acquiring case adaptation knowledge. In this approach, adaptation problems are initially solved by reasoning from scratch, using abstract rules about structural transformations and general memory search heuristics. Traces of the processing used for successful rule-based adaptation are stored as cases to enable future adaptation to be done by case-based reasoning. When similar adaptation problems are encountered in the future, these adaptation cases provide task- and domain-specific guidance for the case adaptation process. We present the tenets of the approach concerning the relationship between memory search and case adaptation, the memory search process, and the storage and reuse of cases representing adaptation episodes. These points are discussed in the context of ongoing research on DIAL, a computer model that learns case adaptation knowledge for case-based disaster response planning.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Case Based
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cora
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1-hop neighbor's text information: Generalized update: Belief change in dynamic settings. : Belief revision and belief update have been proposed as two types of belief change serving different purposes. Belief revision is intended to capture changes of an agent's belief state reflecting new information about a static world. Belief update is intended to capture changes of belief in response to a changing world. We argue that both belief revision and belief update are too restrictive; routine belief change involves elements of both. We present a model for generalized update that allows updates in response to external changes to inform the agent about its prior beliefs. This model of update combines aspects of revision and update, providing a more realistic characterization of belief change. We show that, under certain assumptions, the original update postulates are satisfied. We also demonstrate that plain revision and plain update are special cases of our model, in a way that formally verifies the intuition that revision is suitable for static belief change.
1-hop neighbor's text information: On the logic of iterated belief revision. : We show in this paper that the AGM postulates are too week to ensure the rational preservation of conditional beliefs during belief revision, thus permitting improper responses to sequences of observations. We remedy this weakness by proposing four additional postulates, which are sound relative to a qualitative version of probabilistic conditioning. Contrary to the AGM framework, the proposed postulates characterize belief revision as a process which may depend on elements of an epistemic state that are not necessarily captured by a belief set. We also show that a simple modification to the AGM framework can allow belief revision to be a function of epistemic states. We establish a model-based representation theorem which characterizes the proposed postulates and constrains, in turn, the way in which entrenchment orderings may be transformed under iterated belief revision.
Target text information: Iterated revision and minimal revision of conditional beliefs. : We describe a model of iterated belief revision that extends the AGM theory of revision to account for the effect of a revision on the conditional beliefs of an agent. In particular, this model ensures that an agent makes as few changes as possible to the conditional component of its belief set. Adopting the Ramsey test, minimal conditional revision provides acceptance conditions for arbitrary right-nested conditionals. We show that problem of determining acceptance of any such nested conditional can be reduced to acceptance tests for unnested conditionals. Thus, iterated revision can be accomplished in a virtual manner, using uniterated revision.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Probabilistic Methods
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cora
| 2,486
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test
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1-hop neighbor's text information: Automatic Definition of Modular Neural Networks. Adaptive Behavior, :
1-hop neighbor's text information: Genetic programming with user-driven selection: Experiments on the evolution of algorithms for image enhancement. : This paper describes an approach to using GP for image analysis based on the idea that image enhancement, feature detection and image segmentation can be re-framed as image filtering problems. GP can be used to discover efficient optimal filters which solve such problems. However, in order to make the search feasible and effective, terminal sets, function sets and fitness functions have to meet some requirements. In the paper these requirements are described and terminals, functions and fitness functions that satisfy them are proposed. Some preliminary experiments are also reported in which GP (with the mentioned characteristics) is applied to the segmentation of the brain in magnetic resonance images (an extremely difficult problem for which no simple solution is known) and compared with artificial neural nets.
Target text information: Cellular encoding for interactive evolutionary robotics. : Research in robotics programming is divided in two camps. The direct hand programmming approach uses an explicit model or a behavioral model ( subsumption architecture). The machine learning community uses neural network and/or genetic algorithm. We claim that hand programming and learning are complementary. The two approaches used together can be orders of magnitude more powerful than each approach taken separately. We propose a method to combine them both. It includes three concepts : syntactic constraints to restrict the search space, hand-made problem decomposition, hand given fitness. We use this method to solve a complex problem ( eight-legged locomotion). It needs 5000 less evaluations compared to when genetic algorithm are used alone.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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3
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Genetic Algorithms
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cora
| 2,102
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val
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1-hop neighbor's text information: On-line adaptive critic for changing systems. : In this paper we propose a reactive critic, that is able to respond to changing situations. We will explain why this is usefull in reinforcement learning, where the critic is used to improve the control strategy. We take a problem for which we can derive the solution analytically. This enables us to investigate the relation between the parameters and the resulting approximations of the critic. We will also demonstrate how the reactive critic reponds to changing situations.
1-hop neighbor's text information: AVERAGED REWARD REINFORCEMENT LEARNING APPLIED TO FUZZY RULE TUNING: Fuzzy rules for control can be effectively tuned via reinforcement learning. Reinforcement learning is a weak learning method, which only requires information on the success or failure of the control application. The tuning process allows people to generate fuzzy rules which are unable to accurately perform control and have them tuned to be rules which provide smooth control. This paper explores a new simplified method of using reinforcement learning for the tuning of fuzzy control rules. It is shown that the learned fuzzy rules provide smoother control in the pole balancing domain than another approach.
1-hop neighbor's text information: Fast Online Q(): Q()-learning uses TD()-methods to accelerate Q-learning. The update complexity of previous online Q() implementations based on lookup-tables is bounded by the size of the state/action space. Our faster algorithm's update complexity is bounded by the number of actions. The method is based on the observation that Q-value updates may be postponed until they are needed.
Target text information: Truncating temporal differences: On the efficient implementation of TD() for reinforcement learning. : Temporal difference (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor . Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the efficient and general implementation of TD() for arbitrary , for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suffer from both inefficiency and lack of generality. The TTD (Truncated Temporal Differences) procedure is proposed as an alternative, that indeed only approximates TD(), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using > 0 with the TTD procedure allows one to obtain a significant learning speedup at essentially the same cost as usual TD(0) learning.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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5
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Reinforcement Learning
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cora
| 1,510
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1-hop neighbor's text information: Neuronlike adaptive elements that can solve difficult learning control problems. : Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic neural networks. Technical Report FKI-129-90, Technische Universitat Munchen, Institut fu Informatik. Servan-Schreiber, D., Cleermans, A., and McClelland, J. (1988). Encoding sequential structure in simple recurrent networks. Technical Report CMU-CS-88-183, Carnegie Mellon University, Computer Science Department.
1-hop neighbor's text information: Self-Organization and Associative Memory, : Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feed-forward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedfor-ward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response.
1-hop neighbor's text information: Reinforcement learning with replacing eligibility traces. : The eligibility trace is one of the basic mechanisms used in reinforcement learning to handle delayed reward. In this paper we introduce a new kind of eligibility trace, the replacing trace, analyze it theoretically, and show that it results in faster, more reliable learning than the conventional trace. Both kinds of trace assign credit to prior events according to how recently they occurred, but only the conventional trace gives greater credit to repeated events. Our analysis is for conventional and replace-trace versions of the o*ine TD(1) algorithm applied to undiscounted absorbing Markov chains. First, we show that these methods converge under repeated presentations of the training set to the same predictions as two well known Monte Carlo methods. We then analyze the relative efficiency of the two Monte Carlo methods. We show that the method corresponding to conventional TD is biased, whereas the method corresponding to replace-trace TD is unbiased. In addition, we show that the method corresponding to replacing traces is closely related to the maximum likelihood solution for these tasks, and that its mean squared error is always lower in the long run. Computational results confirm these analyses and show that they are applicable more generally. In particular, we show that replacing traces significantly improve performance and reduce parameter sensitivity on the "Mountain-Car" task, a full reinforcement-learning problem with a continuous state space, when using a feature-based function approximator.
Target text information: Living in a partially structured environment: How to bypass the limitation of classical reinforcement techniques. : In this paper, we propose an unsupervised neural network allowing a robot to learn sensori-motor associations with a delayed reward. The robot task is to learn the "meaning" of pictograms in order to "survive" in a maze. First, we introduce a new neural conditioning rule (PCR: Probabilistic Conditioning Rule) allowing to test hypotheses (associations between visual categories and movements) during a given time span. Second, we describe a real maze experiment with our mobile robot. We propose a neural architecture to solve this problem and we discuss the difficulty to build visual categories dynamically while associating them to movements. Third, we propose to use our algorithm on a simulation in order to test it exhaustively. We give the results for different kind of mazes and we compare our system to an adapted version of the Q-learning algorithm. Finally, we conclude by showing the limitations of approaches that do not take into account the intrinsic complexity of a reasonning based on image recognition.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: An Efficient Metric for Heterogeneous Inductive Learning Applications in the Attribute-Value Language. : Many inductive learning problems can be expressed in the classical attribute-value language. In order to learn and to generalize, learning systems often rely on some measure of similarity between their current knowledge base and new information. The attribute-value language defines a heterogeneous multidimensional input space, where some attributes are nominal and others linear. Defining similarity, or proximity, of two points in such input spaces is non trivial. We discuss two representative homogeneous metrics and show examples of why they are limited to their own domains. We then address the issues raised by the design of a heterogeneous metric for inductive learning systems. In particular, we discuss the need for normalization and the impact of don't-care values. We propose a heterogeneous metric and evaluate it empirically on a simplified version of ILA.
Target text information: Combining Inductive Learning with Prior Knowledge and Reasoning. : Much effort has been devoted to understanding learning and reasoning in artificial intelligence. However, very few models attempt to integrate these two complementary processes. Rather, there is a vast body of research in machine learning, often focusing on inductive learning from examples, quite isolated from the work on reasoning in artificial intelligence. Though these two processes may be different, they are very much interrelated. The ability to reason about a domain of knowledge is often based on rules about that domain, that must be learned somehow. And the ability to reason can often be used to acquire new knowledge, or learn. This paper introduces an Incremental Learning Algorithm (ILA) that attempts to combine inductive learning with prior knowledge and reasoning. ILA has many important characteristics useful for such a combination, including: 1) incremental, self-organizing learning, 2) nonuniform learning, 3) inherent non-monotonicity, 4) extensional and intensional capabilities, and 5) low order polynomial complexity. The paper describes ILA, gives simulation results for several applications, and discusses each of the above characteristics in detail.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Efficient superscalar performance through boosting. : The foremost goal of superscalar processor design is to increase performance through the exploitation of instruction-level parallelism (ILP). Previous studies have shown that speculative execution is required for high instruction per cycle (IPC) rates in non-numerical applications. The general trend has been toward supporting speculative execution in complicated, dynamically-scheduled processors. Performance, though, is more than just a high IPC rate; it also depends upon instruction count and cycle time. Boosting is an architectural technique that supports general speculative execution in simpler, statically-scheduled processors. Boosting labels speculative instructions with their control dependence information. This labelling eliminates control dependence constraints on instruction scheduling while still providing full dependence information to the hardware. We have incorporated boosting into a trace-based, global scheduling algorithm that exploits ILP without adversely affecting the instruction count of a program. We use this algorithm and estimates of the boosting hardware involved to evaluate how much speculative execution support is really necessary to achieve good performance. We find that a statically-scheduled superscalar processor using a minimal implementation of boosting can easily reach the performance of a much more complex dynamically-scheduled superscalar processor.
1-hop neighbor's text information: Limits of Instruction-Level Parallelism, : This paper examines the limits to instruction level parallelism that can be found in programs, in particular the SPEC95 benchmark suite. Apart from using a more recent version of the SPEC benchmark suite, it differs from earlier studies in removing non-essential true dependencies that occur as a result of the compiler employing a stack for subroutine linkage. This is a subtle limitation to parallelism that is not readily evident as it appears as a true dependency on the stack pointer. Other methods can be used that do not employ a stack to remove this dependency. In this paper we show that its removal exposes far more parallelism than has been seen previously. We refer to this type of parallelism as "parallelism at a distance" because it requires impossibly large instruction windows for detection. We conclude with two observations: 1) that a single instruction window characteristic of superscalar machines is inadequate for detecting parallelism at a distance; and 2) in order to take advantage of this parallelism the compiler must be involved, or separate threads must be explicitly programmed.
1-hop neighbor's text information: Limits of control flow on parallelism. : This paper discusses three techniques useful in relaxing the constraints imposed by control flow on parallelism: control dependence analysis, executing multiple flows of control simultaneously, and speculative execution. We evaluate these techniques by using trace simulations to find the limits of parallelism for machines that employ different combinations of these techniques. We have three major results. First, local regions of code have limited parallelism, and control dependence analysis is useful in extracting global parallelism from different parts of a program. Second, a superscalar processor is fundamentally limited because it cannot execute independent regions of code concurrently. Higher performance can be obtained with machines, such as multiprocessors and dataflow machines, that can simultaneously follow multiple flows of control. Finally, without speculative execution to allow instructions to execute before their control dependences are resolved, only modest amounts of parallelism can be obtained for programs with complex control flow.
Target text information: Instructions: Paper and BibTeX entry are available at http://www.complang.tuwien.ac.at/papers/. This paper was published in: Compiler Construction (CC '94), Springer LNCS 786, 1994, pages 158-171 Delayed Exceptions | Speculative Execution of Abstract. Superscalar processors, which execute basic blocks sequentially, cannot use much instruction level parallelism. Speculative execution has been proposed to execute basic blocks in parallel. A pure software approach suffers from low performance, because exception-generating instructions cannot be executed speculatively. We propose delayed exceptions, a combination of hardware and compiler extensions that can provide high performance and correct exception handling in compiler-based speculative execution. Delayed exceptions exploit the fact that exceptions are rare. The compiler assumes the typical case (no exceptions), schedules the code accordingly, and inserts run-time checks and fix-up code that ensure correct execution when exceptions do happen.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Rule Learning
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1-hop neighbor's text information: A Theory of Networks for Approximation and Learning, : Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multi-dimensional function, that is solving the problem of hy-persurface reconstruction. From this point of view, this form of learning is closely related to classical approximation techniques, such as generalized splines and regularization theory. This paper considers the problems of an exact representation and, in more detail, of the approximation of linear and nonlinear mappings in terms of simpler functions of fewer variables. Kolmogorov's theorem concerning the representation of functions of several variables in terms of functions of one variable turns out to be almost irrelevant in the context of networks for learning. We develop a theoretical framework for approximation based on regularization techniques that leads to a class of three-layer networks that we call Generalized Radial Basis Functions (GRBF), since they are mathematically related to the well-known Radial Basis Functions, mainly used for strict interpolation tasks. GRBF networks are not only equivalent to generalized splines, but are also closely related to pattern recognition methods such as Parzen windows and potential functions and to several neural network algorithms, such as Kanerva's associative memory, backpropagation and Kohonen's topology preserving map. They also have an interesting interpretation in terms of prototypes that are synthesized and optimally combined during the learning stage. The paper introduces several extensions and applications of the technique and discusses intriguing analogies with neurobiological data. c fl Massachusetts Institute of Technology, 1994 This paper describes research done within the Center for Biological Information Processing, in the Department of Brain and Cognitive Sciences, and at the Artificial Intelligence Laboratory. This research is sponsored by a grant from the Office of Naval Research (ONR), Cognitive and Neural Sciences Division; by the Artificial Intelligence Center of Hughes Aircraft Corporation; by the Alfred P. Sloan Foundation; by the National Science Foundation. Support for the A. I. Laboratory's artificial intelligence research is provided by the Advanced Research Projects Agency of the Department of Defense under Army contract DACA76-85-C-0010, and in part by ONR contract N00014-85-K-0124.
1-hop neighbor's text information: GAL : Networks that grow when they learn and shrink when they forget, : Learning when limited to modification of some parameters has a limited scope; the capability to modify the system structure is also needed to get a wider range of the learnable. In the case of artificial neural networks, learning by iterative adjustment of synaptic weights can only succeed if the network designer predefines an appropriate network structure, i.e., number of hidden layers, units, and the size and shape of their receptive and projective fields. This paper advocates the view that the network structure should not, as usually done, be determined by trial-and-error but should be computed by the learning algorithm. Incremental learning algorithms can modify the network structure by addition and/or removal of units and/or links. A survey of current connectionist literature is given on this line of thought. "Grow and Learn" (GAL) is a new algorithm that learns an association at one-shot due to being incremental and using a local representation. During the so-called "sleep" phase, units that were previously stored but which are no longer necessary due to recent modifications are removed to minimize network complexity. The incrementally constructed network can later be finetuned off-line to improve performance. Another method proposed that greatly increases recognition accuracy is to train a number of networks and vote over their responses. The algorithm and its variants are tested on recognition of handwritten numerals and seem promising especially in terms of learning speed. This makes the algorithm attractive for on-line learning tasks, e.g., in robotics. The biological plausibility of incremental learning is also discussed briefly. Earlier part of this work was realized at the Laboratoire de Microinformatique of Ecole Polytechnique Federale de Lausanne and was supported by the Fonds National Suisse de la Recherche Scientifique. Later part was realized at and supported by the International Computer Science Institute. A number of people helped by guiding, stimulating discussions or questions: Subutai Ahmad, Peter Clarke, Jerry Feldman, Christian Jutten, Pierre Marchal, Jean Daniel Nicoud, Steve Omohondro and Leon Personnaz.
1-hop neighbor's text information: Self-Organization and Associative Memory, : Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feed-forward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedfor-ward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response.
Target text information: (1995) "Comparison of Kernel Estimators, Perceptrons and Radial-Basis Functions for OCR and Speech Classification," : We compare kernel estimators, single and multi-layered perceptrons and radial-basis functions for the problems of classification of handwritten digits and speech phonemes. By taking two different applications and employing many techniques, we report here a two-dimensional study whereby a domain-independent assessment of these learning methods can be possible. We consider a feed-forward network with one hidden layer. As examples of the local methods, we use kernel estimators like k-nearest neighbor (k-nn), Parzen windows, generalized k-nn, and Grow and Learn (Condensed Nearest Neighbor). We have also considered fuzzy k-nn due to its similarity. As distributed networks, we use linear perceptron, pairwise separating linear perceptron, and multilayer perceptrons with sigmoidal hidden units. We also tested the radial-basis function network which is a combination of local and distributed networks. Four criteria are taken for comparison: Correct classification of the test set, network size, learning time, and the operational complexity. We found that perceptrons when the architecture is suitable, generalize better than local, memory-based kernel estimators but require longer training and more precise computation. Local networks are simple, learn very quickly and acceptably, but use more memory.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Opportunistic Reasoning: A Design Perspective. : An essential component of opportunistic behavior is opportunity recognition, the recognition of those conditions that facilitate the pursuit of some suspended goal. Opportunity recognition is a special case of situation assessment, the process of sizing up a novel situation. The ability to recognize opportunities for reinstating suspended problem contexts (one way in which goals manifest themselves in design) is crucial to creative design. In order to deal with real world opportunity recognition, we attribute limited inferential power to relevant suspended goals. We propose that goals suspended in the working memory monitor the internal (hidden) representations of the currently recognized objects. A suspended goal is satisfied when the current internal representation and a suspended goal match. We propose a computational model for working memory and we compare it with other relevant theories of opportunistic planning. This working memory model is implemented as part of our IMPROVISER system.
1-hop neighbor's text information: Kritik: An early case-based design system. In Maher, M.L. & Pu, : In the late 1980s, we developed one of the early case-based design systems called Kritik. Kritik autonomously generated preliminary (conceptual, qualitative) designs for physical devices by retrieving and adapting past designs stored in its case memory. Each case in the system had an associated structure-behavior-function (SBF) device model that explained how the structure of the device accomplished its functions. These casespecific device models guided the process of modifying a past design to meet the functional specification of a new design problem. The device models also enabled verification of the design modifications. Kritik2 is a new and more complete implementation of Kritik. In this paper, we take a retrospective view on Kritik. In early papers, we had described Kritik as integrating case-based and model-based reasoning. In this integration, Kritik also grounds the computational process of case-based reasoning in the SBF content theory of device comprehension. The SBF models not only provide methods for many specific tasks in case-based design such as design adaptation and verification, but they also provide the vocabulary for the whole process of case-based design, from retrieval of old cases to storage of new ones. This grounding, we believe, is essential for building well-constrained theories of case-based design.
1-hop neighbor's text information: Innovation in Analogical Design: A Model-Based Approach. :
Target text information: Modeling Invention by Analogy in ACT-R: We investigate some aspects of cognition involved in invention, more precisely in the invention of the telephone by Alexander Graham Bell. We propose the use of the Structure-Behavior-Function (SBF) language for the representation of invention knowledge; we claim that because SBF has been shown to support a wide range of reasoning about physical devices, it constitutes a plausible account of how an inventor might represent knowledge of an invention. We further propose the use of the ACT-R architecture for the implementation of this model. ACT-R has been shown to very precisely model a wide range of human cognition. We draw upon the architecture for execution of productions and matching of declarative knowledge through spreading activation. Thus we present a model which combines the well-established cognitive validity of ACT-R with the powerful, specialized model-based reasoning methods facilitated by SBF.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Learning to predict reading frames in E. coli DNA sequences. : Two fundamental problems in analyzing DNA sequences are (1) locating the regions of a DNA sequence that encode proteins, and (2) determining the reading frame for each region. We investigate using artificial neural networks (ANNs) to find coding regions, determine reading frames, and detect frameshift errors in E. coli DNA sequences. We describe our adaptation of the approach used by Uberbacher and Mural to identify coding regions in human DNA, and we compare the performance of ANNs to several conventional methods for predicting reading frames. Our experiments demonstrate that ANNs can outperform these conventional approaches.
1-hop neighbor's text information: Representing and restructuring domain theories: A constructive induction approach. : Theory revision integrates inductive learning and background knowledge by combining training examples with a coarse domain theory to produce a more accurate theory. There are two challenges that theory revision and other theory-guided systems face. First, a representation language appropriate for the initial theory may be inappropriate for an improved theory. While the original representation may concisely express the initial theory, a more accurate theory forced to use that same representation may be bulky, cumbersome, and difficult to reach. Second, a theory structure suitable for a coarse domain theory may be insufficient for a fine-tuned theory. Systems that produce only small, local changes to a theory have limited value for accomplishing complex structural alterations that may be required. Consequently, advanced theory-guided learning systems require flexible representation and flexible structure. An analysis of various theory revision systems and theory-guided learning systems reveals specific strengths and weaknesses in terms of these two desired properties. Designed to capture the underlying qualities of each system, a new system uses theory-guided constructive induction. Experiments in three domains show improvement over previous theory-guided systems. This leads to a study of the behavior, limitations, and potential of theory-guided constructive induction.
Target text information: Investigating the value of a good input representation. : This paper is reprinted from Computational Learning Theory and Natural Learning Systems, vol. 3, T. Petsche, S. Hanson and J. Shavlik (eds.), 1995. Copyrighted 1995 by MIT Press. Abstract The ability of an inductive learning system to find a good solution to a given problem is dependent upon the representation used for the features of the problem. A number of factors, including training-set size and the ability of the learning algorithm to perform constructive induction, can mediate the effect of an input representation on the accuracy of a learned concept description. We present experiments that evaluate the effect of input representation on generalization performance for the real-world problem of finding genes in DNA. Our experiments that demonstrate that: (1) two different input representations for this task result in significantly different generalization performance for both neural networks and decision trees; and (2) both neural and symbolic methods for constructive induction fail to bridge the gap between these two representations. We believe that this real-world domain provides an interesting challenge problem for the machine learning subfield of constructive induction because the relationship between the two representations is well known, and because conceptually, the representational shift involved in constructing the better representation should not be too imposing.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Learning concepts by asking questions. In R.S. : Tw o important issues in machine learning are explored: the role that memory plays in acquiring new concepts; and the extent to which the learner can take an active part in acquiring these concepts. This chapter describes a program, called Marvin, which uses concepts it has learned previously to learn new concepts. The program forms hypotheses about the concept being learned and tests the hypotheses by asking the trainer questions. Learning begins when the trainer shows Marvin an example of the concept to be learned. The program determines which objects in the example belong to concepts stored in the memory. A description of the new concept is formed by using the information obtained from the memory to generalize the description of the training example. The generalized description is tested when the program constructs new examples and shows these to the trainer, asking if they belong to the target concept.
Target text information: Observation and Generalisation in a Simulated Robot World: This paper describes a program which observes the behaviour of actors in a simulated world and uses these observations as guides to conducting experiments. An experiment is a sequence of actions carried out by an actor in order to support or weaken the case for a generalisation of a concept. A generalisation is attempted when the program observes a state of the world which is similar to a some previous state. A partial matching algorithm is used to find substitutions which enable the two states to be unified. The generalisation of the two states is their unifier.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Case Based
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1-hop neighbor's text information: Evolution of mapmaking ability: Strategies for the evolution of learning, planning, and memory using genetic programming. : An essential component of an intelligent agent is the ability to observe, encode, and use information about its environment. Traditional approaches to Genetic Programming have focused on evolving functional or reactive programs with only a minimal use of state. This paper presents an approach for investigating the evolution of learning, planning, and memory using Genetic Programming. The approach uses a multi-phasic fitness environment that enforces the use of memory and allows fairly straightforward comprehension of the evolved representations . An illustrative problem of 'gold' collection is used to demonstrate the usefulness of the approach. The results indicate that the approach can evolve programs that store simple representations of their environments and use these representations to produce simple plans.
Target text information: The Evolution of Memory and Mental Models Using Genetic Programming build internal representations of their: This paper applies genetic programming their successive actions. The results show to the evolution of intelligent agents that
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Genetic Algorithms
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1-hop neighbor's text information: A sequential niche technique for multimodal function optimization. : c fl UWCC COMMA Technical Report No. 93001, February 1993 x No part of this article may be reproduced for commercial purposes. Abstract A technique is described which allows unimodal function optimization methods to be extended to efficiently locate all optima of multimodal problems. We describe an algorithm based on a traditional genetic algorithm (GA). This involves iterating the GA, but uses knowledge gained during one iteration to avoid re-searching, on subsequent iterations, regions of problem space where solutions have already been found. This is achieved by applying a fitness derating function to the raw fitness function, so that fitness values are depressed in the regions of the problem space where solutions have already been found. Consequently, the likelihood of discovering a new solution on each iteration is dramatically increased. The technique may be used with various styles of GA, or with other optimization methods, such as simulated annealing. The effectiveness of the algorithm is demonstrated on a number of multimodal test functions. The technique is at least as fast as fitness sharing methods. It provides a speedup of between 1 and 10p on a problem with p optima, depending on the value of p and the convergence time complexity.
Target text information: Simple Subpopulation Schemes: This paper considers a new method for maintaining diversity by creating subpopulations in a standard generational evolutionary algorithm. Unlike other methods, it replaces the concept of distance between individuals with tag bits that identify the subpopulation to which an individual belongs. Two variations of this method are presented, illustrating the feasibility of this approach.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Genetic Algorithms
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1-hop neighbor's text information: Strategy Learning with Multilayer Connectionist Represent ations. : Results are presented that demonstrate the learning and fine-tuning of search strategies using connectionist mechanisms. Previous studies of strategy learning within the symbolic, production-rule formalism have not addressed fine-tuning behavior. Here a two-layer connectionist system is presented that develops its search from a weak to a task-specific strategy and fine-tunes its performance. The system is applied to a simulated, real-time, balance-control task. We compare the performance of one-layer and two-layer networks, showing that the ability of the two-layer network to discover new features and thus enhance the original representation is critical to solving the balancing task.
1-hop neighbor's text information: Learning to Play Games from Experience: An Application of Artificial Neural Networks and Temporal Difference Learning. :
1-hop neighbor's text information: Q-learning with hidden-unit restarting. : Platt's resource-allocation network (RAN) (Platt, 1991a, 1991b) is modified for a reinforcement-learning paradigm and to "restart" existing hidden units rather than adding new units. After restarting, units continue to learn via back-propagation. The resulting restart algorithm is tested in a Q-learning network that learns to solve an inverted pendulum problem. Solutions are found faster on average with the restart algorithm than without it.
Target text information: Reinforcement Learning with Modular Neural Networks for Control. : Reinforcement learning methods can be applied to control problems with the objective of optimizing the value of a function over time. They have been used to train single neural networks that learn solutions to whole tasks. Jacobs and Jordan [5] have shown that a set of expert networks combined via a gating network can more quickly learn tasks that can be decomposed. Even the decomposition can be learned. Inspired by Boyan's work of modular neural networks for learning with temporal-difference methods [4], we modify the reinforcement learning algorithm called Q-Learning to train a modular neural network to solve a control problem. The resulting algorithm is demonstrated on the classical pole-balancing problem. The advantage of such a method is that it makes it possible to deal with complex dynamic control problem effectively by using task decomposition and competitive learning.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Reinforcement Learning
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1-hop neighbor's text information: Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions. : We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities are automatically extracted form the knowledge base to facilitate the construction and retraction of plausible beliefs. To represent causation, the formalism incorporates the principle of Markov shielding which imposes a stratified set of independence constraints on rankings of interpretations. We show how this formalism resolves some classical problems associated with specificity, prediction and abduction, and how it offers a natural way of unifying belief revision, belief update, and reasoning about actions.
1-hop neighbor's text information: On the logic of iterated belief revision. : We show in this paper that the AGM postulates are too week to ensure the rational preservation of conditional beliefs during belief revision, thus permitting improper responses to sequences of observations. We remedy this weakness by proposing four additional postulates, which are sound relative to a qualitative version of probabilistic conditioning. Contrary to the AGM framework, the proposed postulates characterize belief revision as a process which may depend on elements of an epistemic state that are not necessarily captured by a belief set. We also show that a simple modification to the AGM framework can allow belief revision to be a function of epistemic states. We establish a model-based representation theorem which characterizes the proposed postulates and constrains, in turn, the way in which entrenchment orderings may be transformed under iterated belief revision.
1-hop neighbor's text information: A qualitative Markov assumption and its implications for belief change. : The study of belief change has been an active area in philosophy and AI. In recent years, two special cases of belief change, belief revision and belief update, have been studied in detail. Roughly speaking, revision treats a surprising observation as a sign that previous beliefs were wrong, while update treats a surprising observation as an indication that the world has changed. In general, we would expect that an agent making an observation may both want to revise some earlier beliefs and assume that some change has occurred in the world. We define a novel approach to belief change that allows us to do this, by applying ideas from probability theory in a qualitative settings. The key idea is to use a qualitative Markov assumption, which says that state transitions are independent. We show that a recent approach to modeling qualitative uncertainty using plausibility measures allows us to make such a qualitative Markov assumption in a relatively straightforward way, and show how the Markov assumption can be used to provide an attractive belief-change model.
Target text information: A Knowledge-Based Framework for Belief Change, Part II: Revision and Update. : The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper [FH94b] we introduced a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to examine the changes of beliefs over time. In this paper we show how belief revision and belief update can be captured in our framework. This allows us to compare the assumptions made by each method and to better understand the principles underlying them. In particular, it allows us to understand the source of Gardenfors' triviality result for belief revision [Gar86] and suggests a way of mitigating the problem. It also shows that Katsuno and Mendelzon's notion of belief update [KM91a] depends on several strong assumptions that may limit its applicability in AI.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: A generalized approximate cross validation for smoothing splines with non-Gaussian data. :
1-hop neighbor's text information: Smoothing spline ANOVA for exponential families, with application to the Wisconsin Epidemiological Study of Diabetic Retinopathy. :
Target text information: Testing the Generalized Linear Model Null Hypothesis versus `Smooth' Alternatives 1:
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: Neural Network Applicability: Classifying the Problem Space, : The tremendous current effort to propose neurally inspired methods of computation forces closer scrutiny of real world application potential of these models. This paper categorizes applications into classes and particularly discusses features of applications which make them efficiently amenable to neural network methods. Computational machines do deterministic mappings of inputs to outputs and many computational mechanisms have been proposed for problem solutions. Neural network features include parallel execution, adaptive learning, generalization, and fault tolerance. Often, much effort is given to a model and applications which can already be implemented in a much more efficient way with an alternate technology. Neural networks are potentially powerful devices for many classes of applications, but not all. However, it is proposed that the class of applications for which neural networks are efficient is both large and commonly occurring in nature. Comparison of supervised, unsupervised, and generalizing systems is also included.
1-hop neighbor's text information: A Generalizing Adaptive Discriminant Network. : This paper overviews the AA1 (Adaptive Algorithm 1) model of ASOCS the (Adaptive Self - Organizing Concurrent Systems) approach. It also presents promising empirical generalization results of AA1 with actual data. AA1 is a topologically dynamic network which grows to fit the problem being learned. AA1 generalizes in a self-organizing fashion to a network which seeks to find features which discriminate between concepts. Convergence to a training set is both guaranteed and bounded linearly in time.
1-hop neighbor's text information: Towards a General Distributed Platform for Learning and Generalization and Word Perfect Corp. 1 Introduction: Different learning models employ different styles of generalization on novel inputs. This paper proposes the need for multiple styles of generalization to support a broad application base. The Priority ASOCS model (Priority Adaptive Self-Organizing Concurrent System) is overviewed and presented as a potential platform which can support multiple generalization styles. PASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. The PASOCS can operate in either a data processing mode or a learning mode. During data processing mode, the system acts as a parallel hardware circuit. During learning mode, the PASOCS incorporates rules, with attached priorities, which represent the application being learned. Learning is accomplished in a distributed fashion in time logarithmic in the number of rules. The new model has significant learning time and space complexity improvements over previous models. Generalization in a learning system is at best always a guess. The proper style of generalization is application dependent. Thus, one style of generalization may not be sufficient to allow a learning system to support a broad spectrum of applications [14]. Current connectionist models use one specific style of generalization which is implicit in the learning algorithm. We suggest that the type of generalization used be a self-organizing parameter of the learning system which can be discovered as learning takes place. This requires a) a model which allows flexible generalization styles, and b) mechanisms to guide the system into the best style of generalization for the problem being learned. This paper overviews a learning model which seeks to efficiently support requirement a) above. The model is called Priority ASOCS (PASOCS) [9], which is a member of a class of models called ASOCS (Adaptive Self-Organizing Concurrent Systems) [5]. Section 2 of this paper gives an example of how different generalization techniques can approach a problem. Section 3 presents an overview of PASOCS. Section 4 illustrates how flexible generalization can be supported. Section 5 concludes the paper.
Target text information: A self-organizing binary decision tree for incrementally defined rule based systems. : This paper presents an ASOCS (adaptive self-organizing concurrent system) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on adaptive algorithm 3 (AA3) and details its architecture and learning algorithm. It has advantages over previous ASOCS models in simplicity, implementability, and cost. An ASOCS can operate in either a data processing mode or a learning mode. During the data processing mode, an ASOCS acts as a parallel hardware circuit. In learning mode, rules expressed as boolean conjunctions are incrementally presented to the ASOCS. All ASOCS learning algorithms incorporate a new rule in a distributed fashion in a short, bounded time.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: Combining data mining and machine learning for effective user profiling. : This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specifically, we use a rule- learning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned methods for detecting fraud.
1-hop neighbor's text information: Learning decision lists using homogeneous rules. : rules (Rivest 1987). Inductive algorithms such as AQ and CN2 learn decision lists incrementally, one rule at a time. Such algorithms face the rule overlap problem | the classification accuracy of the decision list depends on the overlap between the learned rules. Thus, even though the rules are learned in isolation, they can only be evaluated in concert. Existing algorithms solve this problem by adopting a greedy, iterative structure. Once a rule is learned, the training examples that match the rule are removed from the training set. We propose a novel solution to the problem: composing decision lists from homogeneous rules, rules whose classification accuracy does not change with their position in the decision list. We prove that the problem of finding a maximally accurate decision list can be reduced to the problem of finding maximally accurate homogeneous rules. We report on the performance of our algorithm on data sets from the UCI repository and on the MONK's problems.
Target text information: (1997) Adaptive fraud detection. Data Mining and Knowledge Discovery, : One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Rule Learning
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1-hop neighbor's text information: A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization: In this work, we present a new bottom-up algorithm for decision tree pruning that is very efficient (requiring only a single pass through the given tree), and prove a strong performance guarantee for the generalization error of the resulting pruned tree. We work in the typical setting in which the given tree T may have been derived from the given training sample S, and thus may badly overfit S. In this setting, we give bounds on the amount of additional generalization error that our pruning suffers compared to the optimal pruning of T . More generally, our results show that if there is a pruning of T with small error, and whose size is small compared to jSj, then our algorithm will find a pruning whose error is not much larger. This style of result has been called an index of resolvability result by Barron and Cover in the context of density estimation. A novel feature of our algorithm is its locality | the decision to prune a subtree is based entirely on properties of that subtree and the sample reaching it. To analyze our algorithm, we develop tools of local uniform convergence, a generalization of the standard notion that may prove useful in other settings.
Target text information: On the Boosting Ability of Top-Down Decision Tree Learning Algorithms. : We analyze the performance of top-down algorithms for decision tree learning, such as those employed by the widely used C4.5 and CART software packages. Our main result is a proof that such algorithms are boosting algorithms. By this we mean that if the functions that label the internal nodes of the decision tree can weakly approximate the unknown target function, then the top-down algorithms we study will amplify this weak advantage to build a tree achieving any desired level of accuracy. The bounds we obtain for this amplification show an interesting dependence on the splitting criterion used by the top-down algorithm. More precisely, if the functions used to label the internal nodes have error 1=2 fl as approximations to the target function, then for the splitting criteria used by CART and C4.5, trees of size (1=*) O(1=fl 2 * 2 ) and (1=*) O(log(1=*)=fl 2 ) (respectively) suffice to drive the error below *. Thus (for example), small constant advantage over random guessing is amplified to constant error with trees of constant size. For a new splitting criterion suggested by our analysis, the
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Theory
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1-hop neighbor's text information: Towards a better understanding of memory-based and bayesian classifiers. : We quantify both experimentally and analytically the performance of memory-based reasoning (MBR) algorithms. To start gaining insight into the capabilities of MBR algorithms, we compare an MBR algorithm using a value difference metric to a popular Bayesian classifier. These two approaches are similar in that they both make certain independence assumptions about the data. However, whereas MBR uses specific cases to perform classification, Bayesian methods summarize the data probabilistically. We demonstrate that a particular MBR system called Pebls works comparatively well on a wide range of domains using both real and artificial data. With respect to the artificial data, we consider distributions where the concept classes are separated by functional discriminants, as well as time-series data generated by Markov models of varying complexity. Finally, we show formally that Pebls can learn (in the limit) natural concept classes that the Bayesian classifier cannot learn, and that it will attain perfect accuracy whenever
1-hop neighbor's text information: Sonderforschungsbereich 314 K unstliche Intelligenz Wissensbasierte Systeme KI-Labor am Lehrstuhl f ur Informatik IV Numerical:
1-hop neighbor's text information: Logarithmic Time Parallel Bayesian Inference: I present a parallel algorithm for exact probabilistic inference in Bayesian networks. For polytree networks with n variables, the worst-case time complexity is O(log n) on a CREW PRAM (concurrent-read, exclusive-write parallel random-access machine) with n processors, for any constant number of evidence variables. For arbitrary networks, the time complexity is O(r 3w log n) for n processors, or O(w log n) for r 3w n processors, where r is the maximum range of any variable, and w is the induced width (the maximum clique size), after moralizing and trian gulating the network.
Target text information: Logarithmic-time updates and queries in probabilistic networks. : Traditional databases commonly support efficient query and update procedures that operate in time which is sublinear in the size of the database. Our goal in this paper is to take a first step toward dynamic reasoning in probabilistic databases with comparable efficiency. We propose a dynamic data structure that supports efficient algorithms for updating and querying singly connected Bayesian networks. In the conventional algorithm, new evidence is absorbed in time O(1) and queries are processed in time O(N ), where N is the size of the network. We propose an algorithm which, after a preprocessing phase, allows us to answer queries in time O(log N ) at the expense of O(log N ) time per evidence absorption. The usefulness of sub-linear processing time manifests itself in applications requiring (near) real-time response over large probabilistic databases. We briefly discuss a potential application of dynamic probabilistic reasoning in computational biology.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Probabilistic Methods
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cora
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1-hop neighbor's text information: A theory of questions and question asking. :
1-hop neighbor's text information: Learning indices for schema selection. : In addition to learning new knowledge, a system must be able to learn when the knowledge is likely to be applicable. An index is a piece of information which, when identified in a given situation, triggers the relevant piece of knowledge (or schema) in the system's memory. We discuss the issue of how indices may be learned automatically in the context of a story understanding task, and present a program that can learn new indices for existing explanatory schemas. We discuss two methods using which the system can identify the relevant schema even if the input does not directly match an existing index, and learn a new index to allow it to retrieve this schema more efficiently in the future.
1-hop neighbor's text information: Using Introspective Reasoning to Select Learning Strategies. : In order to learn effectively, a system must not only possess knowledge about the world and be able to improve that knowledge, but it also must introspectively reason about how it performs a given task and what particular pieces of knowledge it needs to improve its performance at the current task. Introspection requires a declaratflive representation of the reasoning performed by the system during the performance task. This paper presents a taxonomy of possible reasoning failures that can occur during this task, their declarative representations, and their associations with particular learning strategies. We propose a theory of Meta-XPs, which are explanation structures that help the system identify failure types and choose appropriate learning strategies in order to avoid similar mistakes in the future. A program called Meta-AQUA embodies the theory and processes examples in the domain of drug smuggling.
Target text information: Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases. : This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-based reasoning is the process of using past experiences stored in the reasoner's memory to understand novel situations or solve novel problems. However, this process assumes that past experiences are well understood and provide good "lessons" to be used for future situations. This assumption is usually false when one is learning about a novel domain, since situations encountered previously in this domain might not have been understood completely. Furthermore, the reasoner may not even have a case that adequately deals with the new situation, or may not be able to access the case using existing indices. We present a theory of incremental learning based on the revision of previously existing case knowledge in response to experiences in such situations. The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c) incrementally refine its understanding of the case by using it to reason about new situations, thus evolving a better understanding of its domain through experience. This research complements work in case-based reasoning by providing mechanisms by which a case library can be automatically built for use by a case-based reasoning program.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Case Based
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1-hop neighbor's text information: "An Optimum Decision Rule for Pattern Recognition," :
Target text information: "On Functional Relation between Recognition Error and Class-Selective Reject," : This report reviews various optimum decision rules for pattern recognition, namely, Bayes rule, Chow's rule (optimum error-reject tradeoff), and a recently proposed class-selective rejection rule. The latter provides an optimum tradeoff between the error rate and the average number of (selected) classes. A new general relation between the error rate and the average number of classes is presented. The error rate can directly be computed from the class-selective reject function, which in turn can be estimated from unlabelled patterns, by simply counting the rejects. Theoretical as well as practical implications are discussed and some future research directions are proposed.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Probabilistic Methods
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1-hop neighbor's text information: Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes. : Reinforcement learning (RL) has become a central paradigm for solving learning-control problems in robotics and artificial intelligence. RL researchers have focussed almost exclusively on problems where the controller has to maximize the discounted sum of payoffs. However, as emphasized by Schwartz (1993), in many problems, e.g., those for which the optimal behavior is a limit cycle, it is more natural and com-putationally advantageous to formulate tasks so that the controller's objective is to maximize the average payoff received per time step. In this paper I derive new average-payoff RL algorithms as stochastic approximation methods for solving the system of equations associated with the policy evaluation and optimal control questions in average-payoff RL tasks. These algorithms are analogous to the popular TD and Q-learning algorithms already developed for the discounted-payoff case. One of the algorithms derived here is a significant variation of Schwartz's R-learning algorithm. Preliminary empirical results are presented to validate these new algorithms.
1-hop neighbor's text information: Value Function Based Production Scheduling: Production scheduling, the problem of sequentially configuring a factory to meet forecasted demands, is a critical problem throughout the manufacturing industry. The requirement of maintaining product inventories in the face of unpredictable demand and stochastic factory output makes standard scheduling models, such as job-shop, inadequate. Currently applied algorithms, such as simulated annealing and constraint propagation, must employ ad-hoc methods such as frequent replanning to cope with uncertainty. In this paper, we describe a Markov Decision Process (MDP) formulation of production scheduling which captures stochasticity in both production and demands. The solution to this MDP is a value function which can be used to generate optimal scheduling decisions online. A simple example illustrates the theoretical superiority of this approach over replanning-based methods. We then describe an industrial application and two reinforcement learning methods for generating an approximate value function on this domain. Our results demonstrate that in both deterministic and noisy scenarios, value function approx imation is an effective technique.
1-hop neighbor's text information: Learning to predict by the methods of temporal differences. : This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods; and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage.
Target text information: "Self-improving factory simulation using continuous-time average reward reinforcement learning" in Proceedings of the Fourteenth International Machine Learning Conference., : Many factory optimization problems, from inventory control to scheduling and reliability, can be formulated as continuous-time Markov decision processes. A primary goal in such problems is to find a gain-optimal policy that minimizes the long-run average cost. This paper describes a new average-reward algorithm called SMART for finding gain-optimal policies in continuous time semi-Markov decision processes. The paper presents a detailed experimental study of SMART on a large unreliable production inventory problem. SMART outperforms two well-known reliability heuristics from industrial engineering. A key feature of this study is the integration of the reinforcement learning algorithm directly into two commercial discrete-event simulation packages, ARENA and CSIM, paving the way for this approach to be applied to many other factory optimization problems for which there already exist simulation models.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Reinforcement Learning
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1-hop neighbor's text information: "On the Markov equivalence of chain graphs, undirected graphs, and acyclic digraphs", : Acyclic digraphs (ADGs) are widely used to describe dependences among variables in multivariate distributions. In particular, the likelihood functions of ADG models admit convenient recursive factorizations that often allow explicit maximum likelihood estimates and that are well suited to building Bayesian networks for expert systems. There may, however, be many ADGs that determine the same dependence (= Markov) model. Thus, the family of all ADGs with a given set of vertices is naturally partitioned into Markov-equivalence classes, each class being associated with a unique statistical model. Statistical procedures, such as model selection or model averaging, that fail to take into account these equivalence classes, may incur substantial computational or other inefficiencies. Recent results have shown that each Markov-equivalence class is uniquely determined by a single chain graph, the essential graph, that is itself Markov-equivalent simultaneously to all ADGs in the equivalence class. Here we propose two stochastic Bayesian model averaging and selection algorithms for essential graphs and apply them to the analysis of three discrete-variable data sets.
1-hop neighbor's text information: Model selection and accounting for model uncertainty in graphical models using Occam\'s window. : We consider the problem of model selection and accounting for model uncertainty in high-dimensional contingency tables, motivated by expert system applications. The approach most used currently is a stepwise strategy guided by tests based on approximate asymptotic P -values leading to the selection of a single model; inference is then conditional on the selected model. The sampling properties of such a strategy are complex, and the failure to take account of model uncertainty leads to underestimation of uncertainty about quantities of interest. In principle, a panacea is provided by the standard Bayesian formalism which averages the posterior distributions of the quantity of interest under each of the models, weighted by their posterior model probabilities. Furthermore, this approach is optimal in the sense of maximising predictive ability. However, this has not been used in practice because computing the posterior model probabilities is hard and the number of models is very large (often greater than 10 11 ). We argue that the standard Bayesian formalism is unsatisfactory and we propose an alternative Bayesian approach that, we contend, takes full account of the true model uncertainty by averaging over a much smaller set of models. An efficient search algorithm is developed for finding these models. We consider two classes of graphical models that arise in expert systems: the recursive causal models and the decomposable fl David Madigan is Assistant Professor of Statistics and Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, GN-22, University of Washington, Seattle, WA 98195. Madigan's research was partially supported by the Graduate School Research Fund, University of Washington and by the NSF. Raftery's research was supported by ONR Contract no. N-00014-91-J-1074. The authors are grateful to Gregory Cooper, Leo Goodman, Shelby Haberman, David Hinkley, Graham Upton, Jon Wellner, Nanny Wermuth, Jeremy York, Walter Zucchini and two anonymous referees for helpful comments and discussions, and to Michael R. Butler for providing the data for the scrotal swellings example.
1-hop neighbor's text information: Graphical Models in Applied Multivariate Statistics. :
Target text information: C.M. (1997). A graphical characterization of lattice conditional independence models. : Lattice conditional independence (LCI) models for multivariate normal data recently have been introduced for the analysis of non-monotone missing data patterns and of nonnested dependent linear regression models ( seemingly unrelated regressions). It is shown here that the class of LCI models coincides with a subclass of the class of graphical Markov models determined by acyclic digraphs (ADGs), namely, the subclass of transitive ADG models. An explicit graph - theoretic characterization of those ADGs that are Markov equivalent to some transitive ADG is obtained. This characterization allows one to determine whether a specific ADG D is Markov equivalent to some transitive ADG, hence to some LCI model, in polynomial time, without an exhaustive search of the (exponentially large) equivalence class [D ]. These results do not require the existence or positivity of joint densities.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Probabilistic Methods
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cora
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test
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1-hop neighbor's text information: Hierarchical Mixtures of Experts and the EM Algorithm, : We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain. *We want to thank Geoffrey Hinton, Tony Robinson, Mitsuo Kawato and Daniel Wolpert for helpful comments on the manuscript. This project was supported in part by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, by by grant IRI-9013991 from the National Science Foundation, and by grant N00014-90-J-1942 from the Office of Naval Research. The project was also supported by NSF grant ASC-9217041 in support of the Center for Biological and Computational Learning at MIT, including funds provided by DARPA under the HPCC program, and NSF grant ECS-9216531 to support an Initiative in Intelligent Control at MIT. Michael I. Jordan is a NSF Presidential Young Investigator.
1-hop neighbor's text information: Gain adaptation beats least squares. : I present computational results suggesting that gain-adaptation algorithms based in part on connectionist learning methods may improve over least squares and other classical parameter-estimation methods for stochastic time-varying linear systems. The new algorithms are evaluated with respect to classical methods along three dimensions: asymptotic error, computational complexity, and required prior knowledge about the system. The new algorithms are all of the same order of complexity as LMS methods, O(n), where n is the dimensionality of the system, whereas least-squares methods and the Kalman filter are O(n 2 ). The new methods also improve over the Kalman filter in that they do not require a complete statistical model of how the system varies over time. In a simple computational experiment, the new methods are shown to produce asymptotic error levels near that of the optimal Kalman filter and significantly below those of least-squares and LMS methods. The new methods may perform better even than the Kalman filter if there is any error in the filter's model of how the system varies over time.
Target text information: From isolation to cooperation: An alternative view of a system of experts. : We introduce a constructive, incremental learning system for regression problems that models data by means of locally linear experts. In contrast to other approaches, the experts are trained independently and do not compete for data during learning. Only when a prediction for a query is required do the experts cooperate by blending their individual predictions. Each expert is trained by minimizing a penalized local cross val i- dation error using second order methods. In this way, an expert is able to find a local distance metric by adjusting the size and shape of the rece p- tive field in which its predictions are valid, and also to detect relevant i n- put features by adjusting its bias on the importance of individual input dimensions. We derive asymptotic results for our method. In a variety of simulations the properties of the algorithm are demonstrated with respect to interference, learning speed, prediction accuracy, feature detection, and task oriented incremental learning.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Data Structures and Genetic Programming, : It is established good software engineering practice to ensure that programs use memory via abstract data structures such as stacks, queues and lists. These provide an interface between the program and memory, freeing the program of memory management details which are left to the data structures to implement. The main result presented herein is that GP can automatically generate stacks and queues. Typically abstract data structures support multiple operations, such as put and get. We show that GP can simultaneously evolve all the operations of a data structure by implementing each such operation with its own independent program tree. That is, the chromosome consists of a fixed number of independent program trees. Moreover, crossover only mixes genetic material of program trees that implement the same operation. Program trees interact with each other only via shared memory and shared "Automatically Defined Functions" (ADFs).
1-hop neighbor's text information: "The Schema Theorem and Price\'s Theorem," : Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has no implications for how well a GA is performing. Interpretations of the Schema Theorem have implicitly assumed that a correlation exists between parent and offspring fitnesses, and this assumption is made explicit in results based on Price's Covariance and Selection Theorem. Schemata do not play a part in the performance theorems derived for representations and operators in general. However, schemata re-emerge when recombination operators are used. Using Geiringer's recombination distribution representation of recombination operators, a "missing" schema theorem is derived which makes explicit the intuition for when a GA should perform well. Finally, the method of "adaptive landscape" analysis is examined and counterexamples offered to the commonly used correlation statistic. Instead, an alternative statistic | the transmission function in the fitness domain | is proposed as the optimal statistic for estimating GA performance from limited samples.
1-hop neighbor's text information: Genetic programming with one-point crossover. : We review the main results obtained in the theory of schemata in Genetic Programming (GP) emphasising their strengths and weaknesses. Then we propose a new, simpler definition of the concept of schema for GP which is closer to the original concept of schema in genetic algorithms (GAs). Along with a new form of crossover, one-point crossover, and point mutation this concept of schema has been used to derive an improved schema theorem for GP which describes the propagation of schemata from one generation to the next. We discuss this result and show that our schema theorem is the natural counterpart for GP of the schema theorem for
Target text information: Price\'s theorem and the MAX problem. : We present a detailed analysis of the evolution of GP populations using the problem of finding a program which returns the maximum possible value for a given terminal and function set and a depth limit on the program tree (known as the MAX problem). We confirm the basic message of [ Gathercole and Ross, 1996 ] that crossover together with program size restrictions can be responsible for premature convergence to a sub-optimal solution. We show that this can happen even when the population retains a high level of variety and show that in many cases evolution from the sub-optimal solution to the solution is possible if sufficient time is allowed. In both cases theoretical models are presented and compared with actual runs. Experimental evidence is presented that Price's Covariance and Selection Theorem can be applied to GP populations and the practical effect of program size restrictions are noted. Finally we show that covariance between gene frequency and fitness in the first few generations can be used to predict the course of GP runs.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Supporting conversational case-based reasoning in an integrated reasoning framework. : Conversational case-based reasoning (CCBR) has been successfully used to assist in case retrieval tasks. However, behavioral limitations of CCBR motivate the search for integrations with other reasoning approaches. This paper briefly describes our group's ongoing efforts towards enhancing the inferencing behaviors of a conversational case-based reasoning development tool named NaCoDAE. In particular, we focus on integrating NaCoDAE with machine learning, model-based reasoning, and generative planning modules. This paper defines CCBR, briefly summarizes the integrations, and explains how they enhance the overall system. Our research focuses on enhancing the performance of conversational case-based reasoning (CCBR) systems (Aha & Breslow, 1997). CCBR is a form of case-based reasoning where users initiate problem solving conversations by entering an initial problem description in natural language text. This text is assumed to be a partial rather than a complete problem description. The CCBR system then assists in eliciting refinements of this description and in suggesting solutions. Its primary purpose is to provide a focus of attention for the user so as to quickly provide a solution(s) for their problem. Figure 1 summarizes the CCBR problem solving cycle. Cases in a CCBR library have three components:
1-hop neighbor's text information: Cbet: a case base exploration tool. : CBET is a software tool for the interactive exploration of a case base. CBET is an integrated environment that provides a range of browsing and display functions that make possible knowledge extraction from a set of cases. CBET is motivated by an application to training firemen. Here cases describe past forest fire fighting interventions and CBET is used to detect dependencies between data, acquire practical planning competences, visualize complex data, clustering similar cases. In CBET well rooted Machine Learning techniques for selecting relevant features, clustering cases and forecasting unknown values have been adapted and reused for case base exploration.
1-hop neighbor's text information: Abstraction considered harmful: lazy learning of language processing. :
Target text information: A Review and Comparative Evaluation of Feature Weighting Methods for Lazy Learning Algorithms, : Many case-based reasoning algorithms retrieve cases using a derivative of the k-nearest neighbor (k-NN) classifier, whose similarity function is sensitive to irrelevant, interacting, and noisy features. Many proposed methods for reducing this sensitivity parameterize k-NN's similarity function with feature weights. We focus on methods that automatically assign weight settings using little or no domain-specific knowledge. Our goal is to predict the relative capabilities of these methods for specific dataset characteristics. We introduce a five-dimensional framework that categorizes automated weight-setting methods, empirically compare methods along one of these dimensions, summarize our results with four hypotheses, and describe additional evidence that supports them. Our investigation revealed that most methods correctly assign low weights to completely irrelevant features, and methods that use performance feedback demonstrate three advantages over other methods (i.e., they require less pre-processing, better tolerate interacting features, and in crease learning rate).
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Structural Regression Trees: In many real-world domains the task of machine learning algorithms is to learn a theory predicting numerical values. In particular several standard test domains used in Inductive Logic Programming (ILP) are concerned with predicting numerical values from examples and relational and mostly non-determinate background knowledge. However, so far no ILP algorithm except one can predict numbers and cope with non-determinate background knowledge. (The only exception is a covering algorithm called FORS.) In this paper we present Structural Regression Trees (SRT), a new algorithm which can be applied to the above class of problems by integrating the statistical method of regression trees into ILP. SRT constructs a tree containing a literal (an atomic formula or its negation) or a conjunction of literals in each node, and assigns a numerical value to each leaf. SRT provides more comprehensible results than purely statistical methods, and can be applied to a class of problems most other ILP systems cannot handle. Experiments in several real-world domains demonstrate that the approach is competitive with existing methods, indicating that the advantages are not at the expense of predictive accuracy.
1-hop neighbor's text information: Irrelevant features and the subset selection problem. : We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection algorithms, and help define the subset of features that should be sought. The features selected should depend not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets.
1-hop neighbor's text information: Knowledge Discovery in International Conflict Databases: In the last decade research in Machine Learning has developed a variety of powerful tools for inductive learning and data analysis. On the other hand, research in International Relations has developed a variety of different conflict databases that are mostly analyzed with classical statistical methods. As these databases are in general of a symbolic nature, they provide an interesting domain for application of Machine Learning algorithms. This paper gives a short overview of available conflict databases and subsequently concentrates on the application of machine learning methods for the analysis and interpretation of such databases.
Target text information: `A case study in machine learning\', : This paper tries to identify rules and factors that are predictive for the outcome of international conflict management attempts. We use C4.5, an advanced Machine Learning algorithm, for generating decision trees and prediction rules from cases in the CONFMAN database. The results show that simple patterns and rules are often not only more understandable, but also more reliable than complex rules. Simple decision trees are able to improve the chances of correctly predicting the outcome of a conflict management attempt. This suggests that mediation is more repetitive than conflicts per se, where such results have not been achieved so far.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: "Evolution in Time and Space: The Parallel Genetic Algorithm." In Foundations of Genetic Algorithms, : The parallel genetic algorithm (PGA) uses two major modifications compared to the genetic algorithm. Firstly, selection for mating is distributed. Individuals live in a 2-D world. Selection of a mate is done by each individual independently in its neighborhood. Secondly, each individual may improve its fitness during its lifetime by e.g. local hill-climbing. The PGA is totally asynchronous, running with maximal efficiency on MIMD parallel computers. The search strategy of the PGA is based on a small number of active and intelligent individuals, whereas a GA uses a large population of passive individuals. We will investigate the PGA with deceptive problems and the traveling salesman problem. We outline why and when the PGA is succesful. Abstractly, a PGA is a parallel search with information exchange between the individuals. If we represent the optimization problem as a fitness landscape in a certain configuration space, we see, that a PGA tries to jump from two local minima to a third, still better local minima, by using the crossover operator. This jump is (probabilistically) successful, if the fitness landscape has a certain correlation. We show the correlation for the traveling salesman problem by a configuration space analysis. The PGA explores implicitly the above correlation.
1-hop neighbor's text information: Local selection. : Local selection (LS) is a very simple selection scheme in evolutionary algorithms. Individual fitnesses are compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. LS, coupled with fitness functions stemming from the consumption of shared environmental resources, maintains diversity in a way similar to fitness sharing; however it is generally more efficient than fitness sharing, and lends itself to parallel implementations for distributed tasks. While LS is not prone to premature convergence, it applies minimal selection pressure upon the population. LS is therefore more appropriate than other, stronger selection schemes only on certain problem classes. This papers characterizes one broad class of problems in which LS consistently out performs tournament selection.
1-hop neighbor's text information: "Using DNA to solve NP-Complete Problems", : A strategy for using Genetic Algorithms (GAs) to solve NP-complete problems is presented. The key aspect of the approach taken is to exploit the observation that, although all NP-complete problems are equally difficult in a general computational sense, some have much better GA representations than others, leading to much more successful use of GAs on some NP-complete problems than on others. Since any NP-complete problem can be mapped into any other one in polynomial time, the strategy described here consists of identifying a canonical NP-complete problem on which GAs work well, and solving other NP-complete problems indirectly by mapping them onto the canonical problem. Initial empirical results are presented which support the claim that the Boolean Satisfiability Problem (SAT) is a GA-effective canonical problem, and that other NP-complete problems with poor GA representations can be solved efficiently by mapping them first onto SAT problems.
Target text information: An Analysis of the Effects of Neighborhood Size and Shape on Local Selecrion Algorithms. : The increasing availability of parallel computing architectures provides an opportunity to exploit this power as we scale up evolutionary algorithms (EAs) to solve more complex problems. To effectively exploit fine grained parallel architectures, the control structure of an EA must be decentralized. This is difficult to achieve without also changing the semantics of the selection algorithm used, which in turn generally produces changes in an EA's problem solving behavior. In this paper we analyze the implications of various decentralized selection algorithms by studying the changes they produce on the characteristics of the selection pressure they induce on the entire population. This approach has resulted in significant insight into the importance of selection variance and local elitism in designing effective distributed selection al gorithms.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Genetic Algorithms
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1-hop neighbor's text information: (1997) Gaussian pro cesses for Bayesian classification via hybrid Monte Carlo. : The full Bayesian method for applying neural networks to a prediction problem is to set up the prior/hyperprior structure for the net and then perform the necessary integrals. However, these integrals are not tractable analytically, and Markov Chain Monte Carlo (MCMC) methods are slow, especially if the parameter space is high-dimensional. Using Gaussian processes we can approximate the weight space integral analytically, so that only a small number of hyperparameters need be integrated over by MCMC methods. We have applied this idea to classification problems, obtaining ex cellent results on the real-world problems investigated so far.
1-hop neighbor's text information: Interpolation Models with Multiple: A traditional interpolation model is characterized by the choice of reg-ularizer applied to the interpolant, and the choice of noise model. Typically, the regularizer has a single regularization constant ff, and the noise model has a single parameter fi. The ratio ff=fi alone is responsible for determining globally all these attributes of the interpolant: its `complexity', `flexibility', `smoothness', `characteristic scale length', and `characteristic amplitude'. We suggest that interpolation models should be able to capture more than just one flavour of simplicity and complexity. We describe Bayesian models in which the interpolant has a smoothness that varies spatially. We emphasize the importance, in practical implementation, of the concept of `conditional convexity' when designing models with many hyperparameters. We apply the new models to the interpolation of neuronal spike data and demonstrate a substantial improvement in generalization error.
1-hop neighbor's text information: Neal (1997). Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification. : Technical Report No. 9702, Department of Statistics, University of Toronto Abstract. Gaussian processes are a natural way of defining prior distributions over functions of one or more input variables. In a simple nonparametric regression problem, where such a function gives the mean of a Gaussian distribution for an observed response, a Gaussian process model can easily be implemented using matrix computations that are feasible for datasets of up to about a thousand cases. Hyperparameters that define the covariance function of the Gaussian process can be sampled using Markov chain methods. Regression models where the noise has a t distribution and logistic or probit models for classification applications can be implemented by sampling as well for latent values underlying the observations. Software is now available that implements these methods using covariance functions with hierarchical parameterizations. Models defined in this way can discover high-level properties of the data, such as which inputs are relevant to predicting the response.
Target text information: Rasmussen (1996). Evaluation of Gaussian Processes and Other Methods for Nonlinear Regression. :
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: Causality in genetic programming. : Machine learning aims towards the acquisition of knowledge based on either experience from the interaction with the external environment or by analyzing the internal problem-solving traces. Both approaches can be implemented in the Genetic Programming (GP) paradigm. [Hillis, 1990] proves in an ingenious way how the first approach can work. There have not been any significant tests to prove that GP can take advantage of its own search traces. This paper presents an approach to automatic discovery of functions in GP based on the ideas of discovery of useful building blocks by analyzing the evolution trace, generalizing of blocks to define new functions and finally adapting of the problem representation on-the-fly. Adaptation of the representation determines a hierarchical organization of the extended function set which enables a restructuring of the search space so that solutions can be found more easily. Complexity measures of solution trees are defined for an adaptive representation framework and empirical results are presented. This material is based on work supported by the National Science Foundation under Grant numbered IRI-8903582 by NIH/PHS research grant numbered 1 R24 RR06853-02 and by a Human Science Frontiers Program research grant. The government has certain rights in this material.
1-hop neighbor's text information: Har-vey (1993) Evolving Visually Guided Robots. : A version of this paper appears in: Proceedings of SAB92, the Second International Conference on Simulation of Adaptive Behaviour J.-A. Meyer, H. Roitblat, and S. Wilson, editors, MIT Press Bradford Books, Cambridge, MA, 1993.
1-hop neighbor's text information: Genetic Algorithms in Search, Optimization and Machine Learning. : Angeline, P., Saunders, G. and Pollack, J. (1993) An evolutionary algorithm that constructs recurrent neural networks, LAIR Technical Report #93-PA-GNARLY, Submitted to IEEE Transactions on Neural Networks Special Issue on Evolutionary Programming.
Target text information: Evolving Visual Routines Architecture and Planning,:
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Genetic Algorithms
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1-hop neighbor's text information: Mean field theory for sigmoid belief networks. : We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition|the classification of handwritten digits.
1-hop neighbor's text information: On convergence properties of the em algorithm for gaussian mixtures. : We build up the mathematical connection between the "Expectation-Maximization" (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix P , and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of P and provide new results analyzing the effect that P has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models.
1-hop neighbor's text information: Ensemble learning for hidden Markovmodels. : The standard method for training Hidden Markov Models optimizes a point estimate of the model parameters. This estimate, which can be viewed as the maximum of a posterior probability density over the model parameters, may be susceptible to over-fitting, and contains no indication of parameter uncertainty. Also, this maximum may be unrepresentative of the posterior probability distribution. In this paper we study a method in which we optimize an ensemble which approximates the entire posterior probability distribution. The ensemble learning algorithm requires the same The traditional training algorithm for hidden Markov models is an expectation-maximization (EM) algorithm (Dempster et al. 1977) known as the Baum-Welch algorithm. It is a maximum likelihood method, or, with a simple modification, a penalized maximum likelihood method, which can be viewed as maximizing a posterior probability density over the model parameters. Recently, Hinton and van Camp (1993) developed a technique known as ensemble learning (see also MacKay (1995) for a review). Whereas maximum a posteriori methods optimize a point estimate of the parameters, in ensemble learning an ensemble is optimized, so that it approximates the entire posterior probability distribution over the parameters. The objective function that is optimized is a variational free energy (Feynman 1972) which measures the relative entropy between the approximating ensemble and the true distribution. In this paper we derive and test an ensemble learning algorithm for hidden Markov models, building on Neal resources as the traditional Baum-Welch algorithm.
Target text information: A new view of the EM algorithm that justifies incremental and other variants. : The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect to the distribution over the unobserved variables. From this perspective, it is easy to justify an incremental variant of the EM algorithm in which the distribution for only one of the unobserved variables is recalculated in each E step. This variant is shown empirically to give faster convergence in a mixture estimation problem. A variant of the algorithm that exploits sparse conditional distributions is also described, and a wide range of other variant algorithms are also seen to be possible.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Probabilistic Methods
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1-hop neighbor's text information: Massive data discrimination via linear support vector machines. : A linear support vector machine formulation is used to generate a fast, finitely-terminating linear-programming algorithm for discriminating between two massive sets in n-dimensional space, where the number of points can be orders of magnitude larger than n. The algorithm creates a succession of sufficiently small linear programs that separate chunks of the data at a time. The key idea is that a small number of support vectors, corresponding to linear programming constraints with positive dual variables, are carried over between the successive small linear programs, each of which containing a chunk of the data. We prove that this procedure is monotonic and terminates in a finite number of steps at an exact solution that leads to a globally optimal separating plane for the entire dataset. Numerical results on fully dense publicly available datasets, numbering 20,000 to 1 million points in 32-dimensional space, confirm the theoretical results and demonstrate the ability to handle very large problems.
1-hop neighbor's text information: Ridge regression in dual variables. : In this paper we study a dual version of the Ridge Regression procedure. It allows us to perform non-linear regression by constructing a linear regression function in a high dimensional feature space. The feature space representation can result in a large increase in the number of parameters used by the algorithm. In order to combat this "curse of dimensionality", the algorithm allows the use of kernel functions, as used in Support Vector methods. We also discuss a powerful family of kernel functions which is constructed using the ANOVA decomposition method from the kernel corresponding to splines with an infinite number of nodes. This paper introduces a regression estimation algorithm which is a combination of these two elements: the dual version of Ridge Regression is applied to the ANOVA enhancement of the infinite-node splines. Experimental results are then presented (based on the Boston Housing data set) which indicate the performance of this algorithm relative to other algorithms.
Target text information: Support vector machines, reproducing kernel Hilbert spaces and the randomized GACV. : 1 Prepared for the NIPS 97 Workshop on Support Vector Machines. Research sponsored in part by NSF under Grant DMS-9704758 and in part by NEI under Grant R01 EY09946. This is a second revised and corrected version of a report of the same number and title dated November 29, 1997
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: Mechanisms of Emergent Computation in Cellular Automata: We introduce a class of embedded-particle models for describing the emergent computational strategies observed in cellular automata (CAs) that were evolved for performing certain computational tasks. The models are evaluated by comparing their estimated performances with the actual performances of the CAs they model. The results show, via a close quantitative agreement, that the embedded-particle framework captures the main information processing mechanisms of the emergent computation that arise in these evolved CAs.
1-hop neighbor's text information: Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work: We review recent work done by our group on applying genetic algorithms (GAs) to the design of cellular automata (CAs) that can perform computations requiring global coordination. A GA was used to evolve CAs for two computational tasks: density classification and synchronization. In both cases, the GA discovered rules that gave rise to sophisticated emergent computational strategies. These strategies can be analyzed using a "computational mechanics" framework in which "particles" carry information and interactions between particles effects information processing. This framework can also be used to explain the process by which the strategies were designed by the GA. The work described here is a first step in employing GAs to engineer useful emergent computation in decentralized multi-processor systems. It is also a first step in understanding how an evolutionary process can produce complex systems with sophisticated collective computational abilities.
1-hop neighbor's text information: Statistical Dynamics of the Royal Road Genetic Algorithm: Metastability is a common phenomenon. Many evolutionary processes, both natural and artificial, alternate between periods of stasis and brief periods of rapid change in their behavior. In this paper an analytical model for the dynamics of a mutation-only genetic algorithm (GA) is introduced that identifies a new and general mechanism causing metastability in evolutionary dynamics. The GA's population dynamics is described in terms of flows in the space of fitness distributions. The trajectories through fitness distribution space are derived in closed form in the limit of infinite populations. We then show how finite populations induce metastability, even in regions where fitness does not exhibit a local optimum. In particular, the model predicts the occurrence of "fitness epochs"| periods of stasis in population fitness distributions|at finite population size and identifies the locations of these fitness epochs with the flow's hyperbolic fixed points. This enables exact predictions of the metastable fitness distributions during the fitness epochs, as well as giving insight into the nature of the periods of stasis and the innovations between them. All these results are obtained as closed-form expressions in terms of the GA's parameters. An analysis of the Jacobian matrices in the neighborhood of an epoch's metastable fitness distribution allows for the calculation of its stable and unstable manifold dimensions and so reveals the state space's topological structure. More general quantitative features of the dynamics|fitness fluctuation amplitudes, epoch stability, and speed of the innovations|are also determined from the Jacobian eigenvalues. The analysis shows how quantitative predictions for a range of dynamical behaviors, that are specific to the finite population dynamics, can be derived from the solution of the infinite population dynamics. The theoretical predictions are shown to agree very well with statistics from GA simulations. We also discuss the connections of our results with those from population genetics and molecular evolution theory.
Target text information: Evolving globally synchronized cellular automata. : How does an evolutionary process interact with a decentralized, distributed system in order to produce globally coordinated behavior? Using a genetic algorithm (GA) to evolve cellular automata (CAs), we show that the evolution of spontaneous synchronization, one type of emergent coordination, takes advantage of the underlying medium's potential to form embedded particles. The particles, typically phase defects between synchronous regions, are designed by the evolutionary process to resolve frustrations in the global phase. We describe in detail one typical solution discovered by the GA, delineating the discovered synchronization algorithm in terms of embedded particles and their interactions. We also use the particle-level description to analyze the evolutionary sequence by which this solution was discovered. Our results have implications both for understanding emergent collective behavior in natural systems and for the automatic programming of decentralized spatially extended multiprocessor systems.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Genetic Algorithms
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1-hop neighbor's text information: Using path diagrams as a structural equation modeling tool. :
1-hop neighbor's text information: Causal inference, path analysis, and recursive structural equations models. : Lipid Research Clinic Program 84] Lipid Research Clinic Program. The Lipid Research Clinics Coronary Primary Prevention Trial results, parts I and II. Journal of the American Medical Association, 251(3):351-374, January 1984. [Pearl 93] Judea Pearl. Aspects of graphical models connected with causality. Technical Report R-195-LL, Cognitive Systems Laboratory, UCLA, June 1993. Submitted to Biometrika (June 1993). Short version in Proceedings of the 49th Session of the International Statistical Institute: Invited papers, Flo rence, Italy, August 1993, Tome LV, Book 1, pp. 391-401.
1-hop neighbor's text information: Experiments with a regression-based causal induction algorithm. EKSL memo number 94-33, : Covariance information can help an algorithm search for predictive causal models and estimate the strengths of causal relationships. This information should not be discarded after conditional independence constraints are identified, as is usual in contemporary causal induction algorithms. Our fbd algorithm combines covariance information with an effective heuristic to build predictive causal models. We demonstrate that fbd is accurate and efficient. In one experiment we assess fbd's ability to find the best predictors for variables; in another we compare its performance, using many measures, with Pearl and Verma's ic algorithm. And although fbd is based on multiple linear regression, we cite evidence that it performs well on problems that are very difficult for regression algorithms.
Target text information: A theory of inferred causation. : This paper concerns the empirical basis of causation, and addresses the following issues: We propose a minimal-model semantics of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covariations following standard norms of inductive reasoning. We also establish a sound characterization of the conditions under which such a distinction is possible. We provide an effective algorithm for inferred causation and show that, for a large class of data the algorithm can uncover the direction of causal influences as defined above. Finally, we ad dress the issue of non-temporal causation.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Quantifying prior determination knowledge using PAC learning model. : Prior knowledge, or bias, regarding a concept can speed up the task of learning it. Probably Approximately Correct (PAC) learning is a mathematical model of concept learning that can be used to quantify the speed up due to different forms of bias on learning. Thus far, PAC learning has mostly been used to analyze syntactic bias, such as limiting concepts to conjunctions of boolean prepositions. This paper demonstrates that PAC learning can also be used to analyze semantic bias, such as a domain theory about the concept being learned. The key idea is to view the hypothesis space in PAC learning as that consistent with all prior knowledge, syntactic and semantic. In particular, the paper presents a PAC analysis of determinations, a type of relevance knowledge. The results of the analysis reveal crisp distinctions and relations among different determinations, and illustrate the usefulness of an analysis based on the PAC model.
1-hop neighbor's text information: A theory of unsupervised speedup learning, : Speedup learning seeks to improve the efficiency of search-based problem solvers. In this paper, we propose a new theoretical model of speedup learning which captures systems that improve problem solving performance by solving a user-given set of problems. We also use this model to motivate the notion of "batch problem solving," and argue that it is more congenial to learning than sequential problem solving. Our theoretical results are applicable to all serially decomposable domains. We empirically validate our results in the domain of Eight Puzzle. 1
1-hop neighbor's text information: Rationality and Intelligence: The long-term goal of our field is the creation and understanding of intelligence. Productive research in AI, both practical and theoretical, benefits from a notion of intelligence that is precise enough to allow the cumulative development of robust systems and general results. The concept of rational agency has long been considered a leading candidate to fulfill this role. This paper outlines a gradual evolution in the formal conception of rationality that brings it closer to our informal conception of intelligence and simultaneously reduces the gap between theory and practice. Some directions for future research are indicated.
Target text information: A formalization of explanation-based macro-operator learning. : In spite of the popularity of Explanation-Based Learning (EBL), its theoretical basis is not well-understood. Using a generalization of Probably Approximately Correct (PAC) learning to problem solving domains, this paper formalizes two forms of Explanation-Based Learning of macro-operators and proves the sufficient conditions for their success. These two forms of EBL, called "Macro Caching" and "Serial Parsing," respectively exhibit two distinct sources of power or "bias": the sparseness of the solution space and the decomposability of the problem-space. The analysis shows that exponential speedup can be achieved when either of these biases is suitable for a domain. Somewhat surprisingly, it also shows that computing the preconditions of the macro-operators is not necessary to obtain these speedups. The theoretical results are confirmed by experiments in the domain of Eight Puzzle. Our work suggests that the best way to address the utility problem in EBL is to implement a bias which exploits the problem-space structure of the set of domains that one is interested in learning.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Theory
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1-hop neighbor's text information: Learning boxes in high dimension. : DIMACS Technical Report 97-32 July 1997 1 A preliminary version of this paper appeared in the proceedings of the EuroCOLT '97 conference, published in volume 1208 of Lecture Notes in Artificial Intelligence, pages 3-15. Springer-Verlag, 1997. The journal version will appear in Algoritmica. 2 E-mail: [email protected]. http://dimacs.rutgers.edu/~beimel. Part of this research was done while the author was a Ph.D. student at the Technion. 3 E-mail: [email protected]. http://www.cs.technion.ac.il/~eyalk. This research was supported by Technion V.P.R. Fund 120-872 and by Japan Technion Society Research Fund.
1-hop neighbor's text information: PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples. : We describe a polynomial-time algorithm for learning axis-aligned rectangles in Q d with respect to product distributions from multiple-instance examples in the PAC model. Here, each example consists of n elements of Q d together with a label indicating whether any of the n points is in the rectangle to be learned. We assume that there is an unknown product distribution D over Q d such that all instances are independently drawn according to D. The accuracy of a hypothesis is measured by the probability that it would incorrectly predict whether one of n more points drawn from D was in the rectangle to be learned. Our algorithm achieves accuracy * with probability 1 ffi in
1-hop neighbor's text information: "Learning unions of two rectangles in the plane with equivalence queries", :
Target text information: Composite geometric concepts and polynomial predictability. :
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Theory
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1-hop neighbor's text information: Unsmearing visual motion: Development of long-range horizontal intrinsic connections. : Human vision systems integrate information nonlocally, across long spatial ranges. For example, a moving stimulus appears smeared when viewed briefly (30 ms), yet sharp when viewed for a longer exposure (100 ms) (Burr, 1980). This suggests that visual systems combine information along a trajectory that matches the motion of the stimulus. Our self-organizing neural network model shows how developmental exposure to moving stimuli can direct the formation of horizontal trajectory-specific motion integration pathways that unsmear representations of moving stimuli. These results account for Burr's data and can potentially also model other phenomena, such as visual inertia.
Target text information: A Theory of Visual Relative Motion Perception: Grouping, Binding, and Gestalt Organization: The human visual system is more sensitive to the relative motion of objects than to their absolute motion. An understanding of motion perception requires an understanding of how neural circuits can group moving visual elements relative to one another, based upon hierarchical reference frames. We have modeled visual relative motion perception using a neural network architecture that groups visual elements according to Gestalt common-fate principles and exploits information about the behavior of each group to predict the behavior of individual elements. A simple competitive neural circuit binds visual elements together into a representation of a visual object. Information about the spiking pattern of neurons allows transfer of the bindings of an object representation from location to location in the neural circuit as the object moves. The model exhibits characteristics of human object grouping and solves some key neural circuit design problems in visual relative motion perception.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: A tutorial on learning Bayesian networks. : Technical Report MSR-TR-95-06
1-hop neighbor's text information: Markov games as a framework for multi-agent reinforcement learning. : In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsistic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic.
1-hop neighbor's text information: Dynamic Programming and Markov Processes. : The problem of maximizing the expected total discounted reward in a completely observable Markovian environment, i.e., a Markov decision process (mdp), models a particular class of sequential decision problems. Algorithms have been developed for making optimal decisions in mdps given either an mdp specification or the opportunity to interact with the mdp over time. Recently, other sequential decision-making problems have been studied prompting the development of new algorithms and analyses. We describe a new generalized model that subsumes mdps as well as many of the recent variations. We prove some basic results concerning this model and develop generalizations of value iteration, policy iteration, model-based reinforcement-learning, and Q-learning that can be used to make optimal decisions in the generalized model under various assumptions. Applications of the theory to particular models are described, including risk-averse mdps, exploration-sensitive mdps, sarsa, Q-learning with spreading, two-player games, and approximate max picking via sampling. Central to the results are the contraction property of the value operator and a stochastic-approximation theorem that reduces asynchronous convergence to synchronous convergence.
Target text information: Learning conventions in multiagent stochastic domains using likelihood estimates. : Fully cooperative multiagent systemsthose in which agents share a joint utility modelis of special interest in AI. A key problem is that of ensuring that the actions of individual agents are coordinated, especially in settings where the agents are autonomous decision makers. We investigate approaches to learning coordinated strategies in stochastic domains where an agent's actions are not directly observable by others. Much recent work in game theory has adopted a Bayesian learning perspective to the more general problem of equilibrium selection, but tends to assume that actions can be observed. We discuss the special problems that arise when actions are not observable, including effects on rates of convergence, and the effect of action failure probabilities and asymmetries. We also use likelihood estimates as a means of generalizing fictitious play learning models in our setting. Finally, we propose the use of maximum likelihood as a means of removing strategies from consideration, with the aim of convergence to a conventional equilibrium, at which point learning and deliberation can cease.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance: Andrew D. Back was with the Department of Electrical and Computer Engineering, University of Queensland. St. Lucia, Australia. He is now with the Brain Information Processing Group, Frontier Research Program, RIKEN, The Institute of Physical and Chemical Research, 2-1 Hirosawa, Wako-shi, Saitama 351-01, Japan Abstract The performance of neural network simulations is often reported in terms of the mean and standard deviation of a number of simulations performed with different starting conditions. However, in many cases, the distribution of the individual results does not approximate a Gaussian distribution, may not be symmetric, and may be multimodal. We present the distribution of results for practical problems and show that assuming Gaussian distributions can significantly affect the interpretation of results, especially those of comparison studies. For a controlled task which we consider, we find that the distribution of performance is skewed towards better performance for smoother target functions and skewed towards worse performance
1-hop neighbor's text information: Presenting and analyzing the results of AI experiments: Data averaging and data snooping. : Presenting and Analyzing the Results of AI Experiments: Data Averaging and Data Snooping, Proceedings of the Fourteenth National Conference on Artificial Intelligence, AAAI-97, AAAI Press, Menlo Park, California, pp. 362367, 1997. Copyright AAAI. Presenting and Analyzing the Results of AI Experiments: Abstract Experimental results reported in the machine learning AI literature can be misleading. This paper investigates the common processes of data averaging (reporting results in terms of the mean and standard deviation of the results from multiple trials) and data snooping in the context of neural networks, one of the most popular AI machine learning models. Both of these processes can result in misleading results and inaccurate conclusions. We demonstrate how easily this can happen and propose techniques for avoiding these very important problems. For data averaging, common presentation assumes that the distribution of individual results is Gaussian. However, we investigate the distribution for common problems and find that it often does not approximate the Gaussian distribution, may not be symmetric, and may be multimodal. We show that assuming Gaussian distributions can significantly affect the interpretation of results, especially those of comparison studies. For a controlled task, we find that the distribution of performance is skewed towards better performance for smoother target functions and skewed towards worse performance for more complex target functions. We propose new guidelines for reporting performance which provide more information about the actual distribution (e.g. box-whiskers plots). For data snooping, we demonstrate that optimization of performance via experimentation with multiple parameters can lead to significance being assigned to results which are due to chance. We suggest that precise descriptions of experimental techniques can be very important to the evaluation of results, and that we need to be aware of potential data snooping biases when formulating these experimental techniques (e.g. selecting the test procedure). Additionally, it is important to only rely on appropriate statistical tests and to ensure that any assumptions made in the tests are valid (e.g. normality of the distribution).
1-hop neighbor's text information: What size neural network gives optimal generalization? Convergence properties of backpropagation. : Technical Report UMIACS-TR-96-22 and CS-TR-3617 Institute for Advanced Computer Studies University of Maryland College Park, MD 20742 Abstract One of the most important aspects of any machine learning paradigm is how it scales according to problem size and complexity. Using a task with known optimal training error, and a pre-specified maximum number of training updates, we investigate the convergence of the backpropagation algorithm with respect to a) the complexity of the required function approximation, b) the size of the network in relation to the size required for an optimal solution, and c) the degree of noise in the training data. In general, for a) the solution found is worse when the function to be approximated is more complex, for b) oversized networks can result in lower training and generalization error in certain cases, and for c) the use of committee or ensemble techniques can be more beneficial as the level of noise in the training data is increased. For the experiments we performed, we do not obtain the optimal solution in any case. We further support the observation that larger networks can produce better training and generalization error using a face recognition example where a network with many more parameters than training points generalizes better than smaller networks.
Target text information: Lessons in neural network training: Overfitting may be harder than expected. : For many reasons, neural networks have become very popular AI machine learning models. Two of the most important aspects of machine learning models are how well the model generalizes to unseen data, and how well the model scales with problem complexity. Using a controlled task with known optimal training error, we investigate the convergence of the backpropagation (BP) algorithm. We find that the optimal solution is typically not found. Furthermore, we observe that networks larger than might be expected can result in lower training and generalization error. This result is supported by another real world example. We further investigate the training behavior by analyzing the weights in trained networks (excess degrees of freedom are seen to do little harm and to aid convergence), and contrasting the interpolation characteristics of multi-layer perceptron neural networks (MLPs) and polynomial models (overfitting behavior is very different the MLP is often biased towards smoother solutions). Finally, we analyze relevant theory outlining the reasons for significant practical differences. These results bring into question common beliefs about neural network training regarding convergence and optimal network size, suggest alternate guidelines for practical use (lower fear of excess degrees of freedom), and help to direct future work (e.g. methods for creation of more parsimonious solutions, importance of the MLP/BP bias and possibly worse performance of improved training algorithms).
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: Mark (1992), Two Methods for Hierarchy Learning in Reinforcement Environments, : This paper describes two methods for hierarchically organizing temporal behaviors. The first is more intuitive: grouping together common sequences of events into single units so that they may be treated as individual behaviors. This system immediately encounters problems, however, because the units are binary, meaning the behaviors must execute completely or not at all, and this hinders the construction of good training algorithms. The system also runs into difficulty when more than one unit is (or should be) active at the same time. The second system is a hierarchy of transition values. This hierarchy dynamically modifies the values that specify the degree to which one unit should follow another. These values are continuous, allowing the use of gradient descent during learning. Furthermore, many units are active at the same time as part of the system's normal functionings.
1-hop neighbor's text information: Discovering solutions with low kolmogorov complexity and high generalization capability. : Many machine learning algorithms aim at finding "simple" rules to explain training data. The expectation is: the "simpler" the rules, the better the generalization on test data (! Occam's razor). Most practical implementations, however, use measures for "simplicity" that lack the power, universality and elegance of those based on Kolmogorov complexity and Solomonoff's algorithmic probability. Likewise, most previous approaches (especially those of the "Bayesian" kind) suffer from the problem of choosing appropriate priors. This paper addresses both issues. It first reviews some basic concepts of algorithmic complexity theory relevant to machine learning, and how the Solomonoff-Levin distribution (or universal prior) deals with the prior problem. The universal prior leads to a probabilistic method for finding "algorithmically simple" problem solutions with high generalization capability. The method is based on Levin complexity (a time-bounded generalization of Kolmogorov complexity) and inspired by Levin's optimal universal search algorithm. With a given problem, solution candidates are computed by efficient "self-sizing" programs that influence their own runtime and storage size. The probabilistic search algorithm finds the "good" programs (the ones quickly computing algorithmically probable solutions fitting the training data). Simulations focus on the task of discovering "algorithmically simple" neural networks with low Kolmogorov complexity and high generalization capability. It is demonstrated that the method, at least with certain toy problems where it is computationally feasible, can lead to generalization results unmatchable by previous neural net algorithms. Much remains do be done, however, to make large scale applications and "incremental learning" feasible.
1-hop neighbor's text information: Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... : This paper introduces the "incremental self-improvement paradigm". Unlike previous methods, incremental self-improvement encourages a reinforcement learning system to improve the way it learns, and to improve the way it improves the way it learns ..., without significant theoretical limitations | the system is able to "shift its inductive bias" in a universal way. Its major features are: (1) There is no explicit difference between "learning", "meta-learning", and other kinds of information processing. Using a Turing machine equivalent programming language, the system itself occasionally executes self-delimiting, initially highly random "self-modification programs" which modify the context-dependent probabilities of future action sequences (including future self-modification programs). (2) The system keeps only those probability modifications computed by "useful" self-modification programs: those which bring about more payoff (reward, reinforcement) per time than all previous self-modification programs. (3) The computation of payoff per time takes into account all the computation time required for learning | the entire system life is considered: boundaries between learning trials are ignored (if there are any). A particular implementation based on the novel paradigm is presented. It is designed to exploit what conventional digital machines are good at: fast storage addressing, arithmetic operations etc. Experiments illustrate the system's mode of operation. Keywords: Self-improvement, self-reference, introspection, machine-learning, reinforcement learning. Note: This is the revised and extended version of an earlier report from November 24, 1994.
Target text information: ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION difference between time and space is that you can't reuse time.: A reinforcement learning system with limited computational resources interacts with an unrestricted, unknown environment. Its goal is to maximize cumulative reward, to be obtained throughout its limited, unknown lifetime. System policy is an arbitrary modifiable algorithm mapping environmental inputs and internal states to outputs and new internal states. The problem is: in realistic, unknown environments, each policy modification process (PMP) occurring during system life may have unpredictable influence on environmental states, rewards and PMPs at any later time. Existing reinforcement learning algorithms cannot properly deal with this. Neither can naive exhaustive search among all policy candidates | not even in case of very small search spaces. In fact, a reasonable way of measuring performance improvements in such general (but typical) situations is missing. I define such a measure based on the novel "reinforcement acceleration criterion" (RAC). At a given time, RAC is satisfied if the beginning of each completed PMP that computed a currently valid policy modification has been followed by long-term acceleration of average reinforcement intake (the computation time for later PMPs is taken into account). I present a method called "environment-independent reinforcement acceleration" (EIRA) which is guaranteed to achieve RAC. EIRA does neither care whether the system's policy allows for changing itself, nor whether there are multiple, interacting learning systems. Consequences are: (1) a sound theoretical framework for "meta-learning" (because the success of a PMP recursively depends on the success of all later PMPs, for which it is setting the stage). (2) A sound theoretical framework for multi-agent learning. The principles have been implemented (1) in a single system using an assembler-like programming language to modify its own policy, and (2) a system consisting of multiple agents, where each agent is in fact just a connection in a fully recurrent reinforcement learning neural net. A by-product of this research is a general reinforcement learning algorithm for such nets. Preliminary experiments illustrate the theory.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Reinforcement Learning
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1-hop neighbor's text information: A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion. :
1-hop neighbor's text information: How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis 1:
1-hop neighbor's text information: Detecting features in spatial point processes with clutter via model-based clustering. : Technical Report No. 295 Department of Statistics, University of Washington October, 1995 1 Abhijit Dasgupta is a graduate student at the Department of Biostatistics, University of Washington, Box 357232, Seattle, WA 98195-7232, and his e-mail address is [email protected]. Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, and his e-mail address is [email protected]. This research was supported by Office of Naval Research Grant no. N-00014-91-J-1074. The authors are grateful to Peter Guttorp, Girardeau Henderson and Robert Muise for helpful discussions.
Target text information: Principal Curve Clustering with Noise. : Technical Report 317 Department of Statistics University of Washington. 1 Derek Stanford is Graduate Research Assistant and Adrian E. Raftery is Professor of Statistics and Sociology, both at the Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, USA. E-mail: [email protected] and [email protected]. Web: http://www.stat.washington.edu/raftery. This research was supported by ONR grants N00014-96-1-0192 and N00014-96-1-0330. The authors are grateful to Simon Byers, Gilles Celeux and Christian Posse for helpful discussions.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: BCM network develops orientation selectivity and ocular dominance from natural scenes environment. : A two-eye visual environment is used in training a network of BCM neurons. We study the effect of misalignment between the synaptic density functions from the two eyes, on the formation of orientation selectivity and ocular dominance in a lateral inhibition network. The visual environment we use is composed of natural images. We show that for the BCM rule a natural image environment with binocular cortical misalignment is sufficient for producing networks with orientation selective cells and ocular dominance columns. This work is an extension of our previous single cell misalignment model (Shouval et al., 1996).
1-hop neighbor's text information: Neuronal goals: Efficient coding and coincidence detection. : Barlow's seminal work on minimal entropy codes and unsupervised learning is reiterated. In particular, the need to transmit the probability of events is put in a practical neuronal framework for detecting suspicious events. A variant of the BCM learning rule [15] is presented together with some mathematical results suggesting optimal minimal entropy coding.
1-hop neighbor's text information: An Information Maximization Approach to Blind Separation and Blind Deconvolution. : We derive a new self-organising learning algorithm which maximises the information transferred in a network of non-linear units. The algorithm does not assume any knowledge of the input distributions, and is defined here for the zero-noise limit. Under these conditions, information maximisation has extra properties not found in the linear case (Linsker 1989). The non-linearities in the transfer function are able to pick up higher-order moments of the input distributions and perform something akin to true redundancy reduction between units in the output representation. This enables the network to separate statistically independent components in the inputs: a higher-order generalisation of Principal Components Analysis. We apply the network to the source separation (or cocktail party) problem, successfully separating unknown mixtures of up to ten speakers. We also show that a variant on the network architecture is able to perform blind deconvolution (cancellation of unknown echoes and reverberation in a speech signal). Finally, we derive dependencies of information transfer on time delays. We suggest that information max-imisation provides a unifying framework for problems in `blind' signal processing. fl Please send comments to [email protected]. This paper will appear as Neural Computation, 7, 6, 1004-1034 (1995). The reference for this version is: Technical Report no. INC-9501, February 1995, Institute for Neural Computation, UCSD, San Diego, CA 92093-0523.
Target text information: Field, "Natural image statistics and efficient coding", : Natural images contain characteristic statistical regularities that set them apart from purely random images. Understanding what these regularities are can enable natural images to be coded more efficiently. In this paper, we describe some of the forms of structure that are contained in natural images, and we show how these are related to the response properties of neurons at early stages of the visual system. Many of the important forms of structure require higher-order (i.e., more than linear, pairwise) statistics to characterize, which makes models based on linear Hebbian learning, or principal components analysis, inappropriate for finding efficient codes for natural images. We suggest that a good objective for an efficient coding of natural scenes is to maximize the sparseness of the representation, and we show that a network that learns sparse codes of natural scenes succeeds in developing localized, oriented, bandpass receptive fields similar to those in the primate striate cortex.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: Model selection and accounting for model uncertainty in graphical models using Occam\'s window. : We consider the problem of model selection and accounting for model uncertainty in high-dimensional contingency tables, motivated by expert system applications. The approach most used currently is a stepwise strategy guided by tests based on approximate asymptotic P -values leading to the selection of a single model; inference is then conditional on the selected model. The sampling properties of such a strategy are complex, and the failure to take account of model uncertainty leads to underestimation of uncertainty about quantities of interest. In principle, a panacea is provided by the standard Bayesian formalism which averages the posterior distributions of the quantity of interest under each of the models, weighted by their posterior model probabilities. Furthermore, this approach is optimal in the sense of maximising predictive ability. However, this has not been used in practice because computing the posterior model probabilities is hard and the number of models is very large (often greater than 10 11 ). We argue that the standard Bayesian formalism is unsatisfactory and we propose an alternative Bayesian approach that, we contend, takes full account of the true model uncertainty by averaging over a much smaller set of models. An efficient search algorithm is developed for finding these models. We consider two classes of graphical models that arise in expert systems: the recursive causal models and the decomposable fl David Madigan is Assistant Professor of Statistics and Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, GN-22, University of Washington, Seattle, WA 98195. Madigan's research was partially supported by the Graduate School Research Fund, University of Washington and by the NSF. Raftery's research was supported by ONR Contract no. N-00014-91-J-1074. The authors are grateful to Gregory Cooper, Leo Goodman, Shelby Haberman, David Hinkley, Graham Upton, Jon Wellner, Nanny Wermuth, Jeremy York, Walter Zucchini and two anonymous referees for helpful comments and discussions, and to Michael R. Butler for providing the data for the scrotal swellings example.
1-hop neighbor's text information: A practical Bayesian framework for backpropagation networks. : A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible: (1) objective comparisons between solutions using alternative network architectures; (2) objective stopping rules for network pruning or growing procedures; (3) objective choice of magnitude and type of weight decay terms or additive regularisers (for penalising large weights, etc.); (4) a measure of the effective number of well-determined parameters in a model; (5) quantified estimates of the error bars on network parameters and on network output; (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian `evidence' automatically embodies `Occam's razor,' penalising over-flexible and over-complex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalisation ability This paper makes use of the Bayesian framework for regularisation and model comparison described in the companion paper `Bayesian interpolation' (MacKay, 1991a). This framework is due to Gull and Skilling (Gull, 1989a). and the Bayesian evidence is obtained.
Target text information: Parallel Markov chain Monte Carlo sampling.: Markov chain Monte Carlo (MCMC) samplers have proved remarkably popular as tools for Bayesian computation. However, problems can arise in their application when the density of interest is high dimensional and strongly correlated. In these circumstances the sampler may be slow to traverse the state space and mixing is poor. In this article we offer a partial solution to this problem. The state space of the Markov chain is augmented to accommodate multiple chains in parallel. Updates to individual chains are based around a genetic style crossover operator acting on `parent' states drawn from the population of chains. This process makes efficient use of gradient information implicitly encoded within the distribution of states across the population. Empirical studies support the claim that the crossover operator acting on a parallel population of chains improves mixing. This is illustrated with an example of sampling a high dimensional posterior probability density from a complex predictive model. By adopting a latent variable approach the methodology is extended to deal with variable selection and model averaging in high dimensions. This is illustrated with an example of knot selection for a spline interpolant.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Self-Organization and Associative Memory, : Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feed-forward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedfor-ward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response.
1-hop neighbor's text information: "Analysis of Linsker\'s simulations of Hebbian rules," : Linsker has reported the development of structured receptive fields in simulations using a Hebb-type synaptic plasticity rule in a feed-forward linear network. The synapses develop under dynamics determined by a matrix that is closely related to the covariance matrix of input cell activities. We analyse the dynamics of the learning rule in terms of the eigenvectors of this matrix. These eigenvectors represent independently evolving weight structures. Some general theorems are presented regarding the properties of these eigenvectors and their eigenvalues. For a general covariance matrix four principal parameter regimes are predicted. We concentrate on the gaussian covariances at layer B ! C of Linsker's network. Analytic and numerical solutions for the eigenvectors at this layer are presented. Three eigenvectors dominate the dynamics: a DC eigenvector, in which all synapses have the same sign; a bi-lobed, oriented eigenvector; and a circularly symmetric, centre-surround eigenvector. Analysis of the circumstances in which each of these vectors dominates yields an explanation of the emergence of centre-surround structures and symmetry-breaking bi-lobed structures. Criteria are developed estimating the boundary of the parameter regime in which centre-surround structures emerge. The application of our analysis to Linsker's higher layers, at which the covariance functions were oscillatory, is briefly discussed.
Target text information: The Role of Constraints in Hebbian Learning: Models of unsupervised correlation-based (Hebbian) synaptic plasticity are typically unstable: either all synapses grow until each reaches the maximum allowed strength, or all synapses decay to zero strength. A common method of avoiding these outcomes is to use a constraint that conserves or limits the total synaptic strength over a cell. We study the dynamical effects of such constraints. Two methods of enforcing a constraint are distinguished, multiplicative and subtractive. For otherwise linear learning rules, multiplicative enforcement of a constraint results in dynamics that converge to the principal eigenvector of the operator determining unconstrained synaptic development. Subtractive enforcement, in contrast, typically leads to a final state in which almost all synaptic strengths reach either the maximum or minimum allowed value. This final state is often dominated by weight configurations other than the principal eigenvector of the unconstrained operator. Multiplicative enforcement yields a "graded" receptive field in which most mutually correlated inputs are represented, whereas subtractive enforcement yields a receptive field that is "sharpened" to a subset of maximally-correlated inputs. If two equivalent input populations (e.g. two eyes) innervate a common target, multiplicative enforcement prevents their segregation (ocular dominance segregation) when the two populations are weakly correlated; whereas subtractive enforcement allows segregation under these circumstances. These results may be used to understand constraints both over output cells and over input cells. A variety of rules that can implement constrained dynamics are discussed.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: A comparison of the fixed and floating building block representation in the genetic algorithm. : This article compares the traditional, fixed problem representation style of a genetic algorithm (GA) with a new floating representation in which the building blocks of a problem are not fixed at specific locations on the individuals of the population. In addition, the effects of non-coding segments on both of these representations is studied. Non-coding segments are a computational model of non-coding DNA and floating building blocks mimic the location independence of genes. The fact that these structures are prevalent in natural genetic systems suggests that they may provide some advantages to the evolutionary process. Our results show that there is a significant difference in how GAs solve a problem in the fixed and floating representations. GAs are able to maintain a more diverse population with the floating representation. The combination of non-coding segments and floating building blocks appears to encourage a GA to take advantage of its parallel search and recombination abilities.
1-hop neighbor's text information: Dynamic control of genetic algorithms using fuzzy logic techniques. : This paper proposes using fuzzy logic techniques to dynamically control parameter settings of genetic algorithms (GAs). We describe the Dynamic Parametric GA: a GA that uses a fuzzy knowledge-based system to control GA parameters. We then introduce a technique for automatically designing and tuning the fuzzy knowledge-base system using GAs. Results from initial experiments show a performance improvement over a simple static GA. One Dynamic Parametric GA system designed by our automatic method demonstrated improvement on an application not included in the design phase, which may indicate the general applicability of the Dynamic Parametric GA to a wide range of ap plications.
1-hop neighbor's text information: A survey of intron research in genetics. : A brief survey of biological research on non-coding DNA is presented here. There has been growing interest in the effects of non-coding segments in evolutionary algorithms (EAs). To better understand and conduct research on non-coding segments and EAs, it is important to understand the biological background of such work. This paper begins with a review of basic genetics and terminology, describes the different types of non-coding DNA, and then surveys recent intron research.
Target text information: Empirical studies of the genetic algorithm with non-coding segments, : The genetic algorithm (GA) is a problem solving method that is modelled after the process of natural selection. We are interested in studying a specific aspect of the GA: the effect of non-coding segments on GA performance. Non-coding segments are segments of bits in an individual that provide no contribution, positive or negative, to the fitness of that individual. Previous research on non-coding segments suggests that including these structures in the GA may improve GA performance. Understanding when and why this improvement occurs will help us to use the GA to its full potential. In this article, we discuss our hypotheses on non-coding segments and describe the results of our experiments. The experiments may be separated into two categories: testing our program on problems from previous related studies, and testing new hypotheses on the effect of non-coding segments.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Genetic Algorithms
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1-hop neighbor's text information: Incremental Learning of Explanation Patterns and their Indices. : This paper describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Recent work in AI has dealt with the issue of using past explanations stored in the reasoner's memory to understand novel situations. However, this process assumes that past explanations are well understood and provide good "lessons" to be used for future situations. This assumption is usually false when one is learning about a novel domain, since situations encountered previously in this domain might not have been understood completely. Instead, it is reasonable to assume that the reasoner would have gaps in its knowledge base. By reasoning about a new situation, the reasoner should be able to fill in these gaps as new information came in, reorganize its explanations in memory, and gradually evolve a better understanding of its domain. We present a story understanding program that retrieves past explanations from situations already in memory, and uses them to build explanations to understand novel stories about terrorism. In doing so, the system refines its understanding of the domain by filling in gaps in these explanations, by elaborating the explanations, or by learning new indices for the explanations. This is a type of incremental learning since the system improves its explanatory knowledge of the domain in an incremental fashion rather than by learning new XPs as a whole.
1-hop neighbor's text information: Innovation in Analogical Design: A Model-Based Approach. :
1-hop neighbor's text information: Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases. : This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-based reasoning is the process of using past experiences stored in the reasoner's memory to understand novel situations or solve novel problems. However, this process assumes that past experiences are well understood and provide good "lessons" to be used for future situations. This assumption is usually false when one is learning about a novel domain, since situations encountered previously in this domain might not have been understood completely. Furthermore, the reasoner may not even have a case that adequately deals with the new situation, or may not be able to access the case using existing indices. We present a theory of incremental learning based on the revision of previously existing case knowledge in response to experiences in such situations. The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c) incrementally refine its understanding of the case by using it to reason about new situations, thus evolving a better understanding of its domain through experience. This research complements work in case-based reasoning by providing mechanisms by which a case library can be automatically built for use by a case-based reasoning program.
Target text information: Learning indices for schema selection. : In addition to learning new knowledge, a system must be able to learn when the knowledge is likely to be applicable. An index is a piece of information which, when identified in a given situation, triggers the relevant piece of knowledge (or schema) in the system's memory. We discuss the issue of how indices may be learned automatically in the context of a story understanding task, and present a program that can learn new indices for existing explanatory schemas. We discuss two methods using which the system can identify the relevant schema even if the input does not directly match an existing index, and learn a new index to allow it to retrieve this schema more efficiently in the future.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Case Based
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1-hop neighbor's text information: Assessment of candidate pfsa models induced from symbol datasets: The induction of the optimal finite state machine explanation from symbol strings is known to be at least NP-complete. However, satisfactory approximately optimal explanations may be found by the use of Evolutionary Programming. It has been shown that an information theoretic measure of finite state machine explanations can be used as the fitness function required for the evaluation of candidate explanations during the search for a near-optimal explanation. It is not obvious from the measure which class of explanation will be favoured over others during the search. By empirical studies it is possible to gain some insight into the dimensions the measure is optimising. In general, for probabilistic finite state machines, explanations assessed by a minimum message length estimator with the minimum number of transitions will be favoured over other explanations. The information measure will also favour explanations with uneven distributions of frequencies on transitions from a node suggesting that repeated sequences in symbol strings will be preferred as an explanation. Approximate bounds for acceptance of explanations and the length of string required for induction to be successful are also derived by considerations of the simplest possible and random explanations and their information measure.
Target text information: (1994) "PFSA Modelling of Behavioural Sequences by Evolutionary Programming" in Stonier, R.J. : Behavioural observations can often be described as a sequence of symbols drawn from a finite alphabet. However the inductive inference of such strings by any automated technique to produce models of the data is a nontrivial task. This paper considers modelling of behavioural data using probabilistic finite state automata (PFSAs). There are a number of information-theoretic techniques for evaluating possible hypotheses. The measure used in this paper is the Minimum Message Length (MML) of Wallace. Although attempts have been made to construct PFSA models by incremental addition of substrings using heuristic rules and the MML to give the lowest information cost, the resultant models cannot be shown to be globally optimal. Fogel's Evolutionary Programming can produce globally optimal PFSA models by evolving data structures of arbitrary complexity without the requirement to encode the PFSA into binary strings as in Genetic Algorithms. However, evaluation of PFSAs during the evolution process by the MML of the PFSA alone is not possible since there will be symbols which cannot be consumed by a partially correct solution. It is suggested that the addition of a "can't consume'' symbol to the symbol alphabet obviates this difficulty. The addition of this null symbol to the alphabet also permits the evolution of explanatory models which need not explain all of the data, a useful property to avoid overfitting noisy data. Results are given for a test set for which the optimal pfsa model is known and for a set of eye glance data derived from an instrument panel simulator.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Theory
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1-hop neighbor's text information: Applications of a logical discovery engine. : The clausal discovery engine claudien is presented. claudien discovers regularities in data and is a representative of the inductive logic programming paradigm. As such, it represents data and regularities by means of first order clausal theories. Because the search space of clausal theories is larger than that of attribute value representation, claudien also accepts as input a declarative specification of the language bias, which determines the set of syntactically well-formed regularities. Whereas other papers on claudien focuss on the semantics or logical problem specification of claudien, on the discovery algorithm, or the PAC-learning aspects, this paper wants to illustrate the power of the resulting technique. In order to achieve this aim, we show how claudien can be used to learn 1) integrity constraints in databases, 2) functional dependencies and determinations, 3) properties of sequences, 4) mixed quantitative and qualitative laws, 5) reverse engineering, and 6) classification rules.
1-hop neighbor's text information: The ilp description learning problem: Towards a genearl model-leve definition of data mining in ilp. : [email protected], [email protected] Proc. FGML-95, Annual Workshop of the GI Special Interest Group Machine Learning (GI FG 1.1.3), ed. K. Morik and J. Herrmann, Research Report 580, Univ.Dortmund, 1995. Abstract The task of discovering interesting regularities in (large) sets of data (data mining, knowledge discovery) has recently met with increased interest in Machine Learning in general and in Inductive Logic Programming (ILP) in particular. However, while there is a widely accepted definition for the task of concept learning from examples in ILP, definitions for the data mining task have been proposed only recently. In this paper, we examine these so-called "non-monotonic semantics" definitions and show that non-monotonicity is only an incidental property of the data mining learning task, and that this task makes perfect sense without such an assumption. We therefore introduce and define a generalized definition of the data mining task called the ILP description learning problem and discuss its properties and relation to the traditional concept learning (prediction) learning problem. Since our characterization is entirely on the level of models, the definition applies independently of the chosen hypothesis language.
Target text information: Application of Clausal Discovery to Temporal Databases: Most of KDD applications consider databases as static objects, and however many databases are inherently temporal, i.e., they store the evolution of each object with the passage of time. Thus, regularities about the dynamics of these databases cannot be discovered as the current state might depend in some way on the previous states. To this end, a pre-processing of data is needed aimed at extracting relationships intimately connected to the temporal nature of data that will be make available to the discovery algorithm. The predicate logic language of ILP methods together with the recent advances as to ef ficiency makes them adequate for this task.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Rule Learning
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1-hop neighbor's text information: (1997) Adaptive fraud detection. Data Mining and Knowledge Discovery, : One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments.
1-hop neighbor's text information: Stochastically Guided Disjunctive Version Space Learning: This paper presents an incremental concept learning approach to identiflcation of concepts with high overall accuracy. The main idea is to address the concept overlap as a central problem when learning multiple descriptions. Many traditional inductive algorithms, as those from the disjunctive version space family considered here, face this problem. The approach focuses on combinations of confldent, possibly overlapping, concepts with an original stochastic complexity formula. The focusing is e-cient because it is organized as a simulated annealing-based beam search. The experiments show that the approach is especially suitable for developing incremental learning algorithms with the following advantages: flrst, it generates highly accurate concepts; second, it overcomes to a certain degree the sensitivity to the order of examples; and third, it handles noisy examples.
1-hop neighbor's text information: Combining data mining and machine learning for effective user profiling. : This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specifically, we use a rule- learning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned methods for detecting fraud.
Target text information: Learning decision lists using homogeneous rules. : rules (Rivest 1987). Inductive algorithms such as AQ and CN2 learn decision lists incrementally, one rule at a time. Such algorithms face the rule overlap problem | the classification accuracy of the decision list depends on the overlap between the learned rules. Thus, even though the rules are learned in isolation, they can only be evaluated in concert. Existing algorithms solve this problem by adopting a greedy, iterative structure. Once a rule is learned, the training examples that match the rule are removed from the training set. We propose a novel solution to the problem: composing decision lists from homogeneous rules, rules whose classification accuracy does not change with their position in the decision list. We prove that the problem of finding a maximally accurate decision list can be reduced to the problem of finding maximally accurate homogeneous rules. We report on the performance of our algorithm on data sets from the UCI repository and on the MONK's problems.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: Evolving Fuzzy Prototypes for Efficient Data Clustering. : number of prototypes used to represent each class, the position of each prototype within its class and the membership function associated with each prototype. This paper proposes a novel, evolutionary approach to data clustering and classification which overcomes many of the limitations of traditional systems. The approach rests on the optimisation of both the number and positions of fuzzy prototypes using a real-valued genetic algorithm (GA). Because the GA acts on all of the classes at once, the system benefits naturally from global information about possible class interactions. In addition, the concept of a receptive field for each prototype is used to replace the classical distance-based membership function by an infinite fuzzy support, multidimensional, Gaussian function centred over the prototype and with unique variance in each dimension, reflecting the tightness of the cluster. Hence, the notion of nearest-neighbour is replaced by that of nearest attracting prototype (NAP). The proposed model is a completely self-optimising, fuzzy system called GA-NAP. Most data clustering algorithms, including the popular K-means algorithm, require a priori knowledge about the problem domain to fix the number and starting positions of the prototypes. Although such knowledge may be assumed for domains whose dimensionality is fairly small or whose underlying structure is relatively intuitive, it is clearly much less accessible in hyper-dimensional settings, where the number of input parameters may be very large. Classical systems also suffer from the fact that they can only define clusters for one class at a time. Hence, no account is made of potential interactions among classes. These drawbacks are further compounded by the fact that the ensuing classification is typically based on a fixed, distance-based membership function for all prototypes. This paper proposes a novel approach to data clustering and classification which overcomes the aforementioned limitations of traditional systems. The model is based on the genetic evolution of fuzzy prototypes. A real-valued genetic algorithm (GA) is used to optimise both the number and positions of prototypes. Because the GA acts on all of the classes at once and measures fitness as classification accuracy, the system naturally profits from global information about class interaction. The concept of a receptive field for each prototype is also presented and used to replace the classical, fixed distance-based function by an infinite fuzzy support membership function. The new membership function is inspired by that used in the hidden layer of RBF networks. It consists of a multidimensional Gaussian function centred over the prototype and with a unique variance in each dimension that reflects the tightness of the cluster. During classification, the notion of nearest-neighbour is replaced by that of nearest attracting prototype (NAP). The proposed model is a completely self-optimising, fuzzy system called GA-NAP.
Target text information: SUPERVISED COMPETITIVE LEARNING FOR FINDING POSITIONS OF RADIAL BASIS FUNCTIONS: This paper introduces the magnetic neural gas (MNG) algorithm, which extends unsupervised competitive learning with class information to improve the positioning of radial basis functions. The basic idea of MNG is to discover heterogeneous clusters (i.e., clusters with data from different classes) and to migrate additional neurons towards them. The discovery is effected by a heterogeneity coefficient associated with each neuron and the migration is guided by introducing a kind of magnetic effect. The performance of MNG is tested on a number of data sets, including the thyroid data set. Results demonstrate promise.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: Improving the performance of evolutionary optimization by dynamically scaling the evolution function. : Traditional evolutionary optimization algorithms assume a static evaluation function, according to which solutions are evolved. Incremental evolution is an approach through which a dynamic evaluation function is scaled over time in order to improve the performance of evolutionary optimization. In this paper, we present empirical results that demonstrate the effectiveness of this approach for genetic programming. Using two domains, a two-agent pursuit-evasion game and the Tracker [6] trail-following task, we demonstrate that incremental evolution is most successful when applied near the beginning of an evolutionary run. We also show that incremental evolution can be successful when the intermediate evaluation functions are more difficult than the target evaluation function, as well as when they are easier than the target function.
1-hop neighbor's text information: "Learning sequential decision rules using simulation models and competition," : The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical decision rules from a simple flight simulator. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Several experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested.
1-hop neighbor's text information: learning easier tasks. More work is necessary in order to determine more precisely the relationship: We have attempted to obtain a stronger correlation between the relationship between G 0 and G 1 and performance. This has included studying the variance in the fitnesses of the members of the population, as well as observing the rate of convergence of the GP with respect to G 1 when a population was evolved for G 0 . 13 Unfortunately, we have not yet been able to obtain a significant correlation. In future work, we plan to to track the genetic diversity (we have only considered phenotypic variance so far) of populations in order to shed some light on the underlying mechanism for priming. One factor that has made this analysis difficult so far is our use of genetic programming, for which the space of genotypes is very large, (i.e., there are many redundant solutions), and for which the neighborhood structure is less easily intuited than that of a standard genetic algorithm. Since there is every reason to believe that the underlying mechanism of incremental evolution is largely independent of the peculiarities of genetic programming, we are currently investigating the incremental evolution mechanism using genetic algorithms with fixed-length genotypes. This should enable a better understanding of the mechanism. Ultimately, we will scale up this research effort to analyze incremental evolution with more than one transition between test cases. This will involve many open issues regarding the optimization of the transition schedule between test cases. 13 We performed the following experiment: Let F it(I; G) be the fitness value of a genetic program I according to the evaluation function G, and Best Of(P op; t; G) be the member I fl of population P op at time t with highest fitness according to G | in other words, I fl = Best Of (P op; t; G) maximizes F it(I; G) over all I 2 P op. A population P op 0 was evolved in the usual manner using evaluation function G 0 for t = 25 generations. However, at each generation 1 i 25 we also evaluated the current population using evaluation function G 1 , and recorded the value of F it(Best Of (P op; i; G 1 ); G 1 ). In other words, we evolved the population using G 0 as the evaluation function, but at every generation we also computed the fitness of the best individual in the population according to G 1 and saved this value. Using the same random seed and control parameters, we then evolved a population P op 1 for t = 30 generations using G 1 as the evaluation function (note that at generation 0, P op 1 is identical to P op 0 ). For all values of t, we compared F it(Best Of (P op 0 ; t; G 1 ); G 1 ) with F it(Best Of (P op 1 ; t; G 1 ); G 1 ). in order to better formalize and exploit this notion of domain difficulty.
Target text information: Adapting the evaluation space to improve global learning. :
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Genetic Algorithms
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1-hop neighbor's text information: Using Markov chains to analyze GAFOs. : Our theoretical understanding of the properties of genetic algorithms (GAs) being used for function optimization (GAFOs) is not as strong as we would like. Traditional schema analysis provides some first order insights, but doesn't capture the non-linear dynamics of the GA search process very well. Markov chain theory has been used primarily for steady state analysis of GAs. In this paper we explore the use of transient Markov chain analysis to model and understand the behavior of finite population GAFOs observed while in transition to steady states. This approach appears to provide new insights into the circumstances under which GAFOs will (will not) perform well. Some preliminary results are presented and an initial evaluation of the merits of this approach is provided.
1-hop neighbor's text information: Evaluating Evolutionary Algorithms. : Test functions are commonly used to evaluate the effectiveness of different search algorithms. However, the results of evaluation are as dependent on the test problems as they are on the algorithms that are the subject of comparison. Unfortunately, developing a test suite for evaluating competing search algorithms is difficult without clearly defined evaluation goals. In this paper we discuss some basic principles that can be used to develop test suites and we examine the role of test suites as they have been used to evaluate evolutionary search algorithms. Current test suites include functions that are easily solved by simple search methods such as greedy hill-climbers. Some test functions also have undesirable characteristics that are exaggerated as the dimensionality of the search space is increased. New methods are examined for constructing functions with different degrees of nonlinearity, where the interactions and the cost of evaluation scale with respect to the dimensionality of the search space.
1-hop neighbor's text information: Modeling Hybrid Genetic Algorithms. : An exact model of a simple genetic algorithm is developed for permutation based representations. Permutation based representations are used for scheduling problems and combinatorial problems such as the Traveling Salesman Problem. A remapping function is developed to remap the model to all permutations in the search space. The mixing matrices for various permutation based operators are also developed.
Target text information: Island Model Genetic Algorithms and Linearly Separable Problems: Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model Genetic Algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. On the other hand, linearly separable functions have often been used to test Island Model Genetic Algorithms; it is possible that Island Models are particular well suited to separable problems. We look at how Island Models can track multiple search trajectories using the infinite population models of the simple genetic algorithm. We also introduce a simple model for better understanding when Island Model Genetic Algorithms may have an advantage when processing linearly separable problems.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Genetic Algorithms
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cora
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1-hop neighbor's text information: "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State Spaces," : Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. In high dimensions it is essential that learning does not plan uniformly over a state-space. Parti-game maintains a decision-tree partitioning of state-space and applies techniques from game-theory and computational geometry to efficiently and adaptively concentrate high resolution only on critical areas. The current version of the algorithm is designed to find feasible paths or trajectories to goal regions in high dimensional spaces. Future versions will be designed to find a solution that optimizes a real-valued criterion. Many simulated problems have been tested, ranging from two-dimensional to nine-dimensional state-spaces, including mazes, path planning, non-linear dynamics, and planar snake robots in restricted spaces. In all cases, a good solution is found in less than ten trials and a few minutes.
1-hop neighbor's text information: Learning to Solve Markovian Decision Processes. :
Target text information: : Multigrid Q-Learning Charles W. Anderson and Stewart G. Crawford-Hines Technical Report CS-94-121 October 11, 1994
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Reinforcement Learning
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1-hop neighbor's text information: Techniques for extracting instruction level parallelism on MIMD architectures. : Extensive research has been done on extracting parallelism from single instruction stream processors. This paper presents some results of our investigation into ways to modify MIMD architectures to allow them to extract the instruction level parallelism achieved by current superscalar and VLIW machines. A new architecture is proposed which utilizes the advantages of a multiple instruction stream design while addressing some of the limitations that have prevented MIMD architectures from performing ILP operation. A new code scheduling mechanism is described to support this new architecture by partitioning instructions across multiple processing elements in order to exploit this level of parallelism.
1-hop neighbor's text information: d d Code Scheduling for Multiple Instruction Stream Architectures: Extensive research has been done on extracting parallelism from single instruction stream processors. This paper presents our investigation into ways to modify MIMD architectures to allow them to extract the instruction level parallelism achieved by current superscalar and VLIW machines. A new architecture is proposed which utilizes the advantages of a multiple instruction stream design while addressing some of the limitations that have prevented MIMD architectures from performing ILP operation. A new code scheduling mechanism is described to support this new architecture by partitioning instructions across multiple processing elements in order to exploit this level of parallelism.
1-hop neighbor's text information: Simultaneous Multithreading: A Platform for Next-Generation Processors. : A version of this paper will appear in ACM Transactions on Computer Systems, August 1997. Permission to make digital copies of part or all of this work for personal or classroom use is grantedwithout fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Abstract To achieve high performance, contemporary computer systems rely on two forms of parallelism: instruction-level parallelism (ILP) and thread-level parallelism (TLP). Wide-issue superscalar processors exploit ILP by executing multiple instructions from a single program in a single cycle. Multiprocessors (MP) exploit TLP by executing different threads in parallel on different processors. Unfortunately, both parallel-processing styles statically partition processor resources, thus preventing them from adapting to dynamically-changing levels of ILP and TLP in a program. With insufficient TLP, processors in an MP will be idle; with insufficient ILP, multiple-issue hardware on a superscalar is wasted. This paper explores parallel processing on an alternative architecture, simultaneous multithreading (SMT), which allows multiple threads to compete for and share all of the processors resources every cycle. The most compelling reason for running parallel applications on an SMT processor is its ability to use thread-level parallelism and instruction-level parallelism interchangeably. By permitting multiple threads to share the processors functional units simultaneously, the processor can use both ILP and TLP to accommodate variations in parallelism. When a program has only a single thread, all of the SMT processors resources can be dedicated to that thread; when more TLP exists, this parallelism can compensate for a lack of
Target text information: a multiple instruction stream computer. : This paper describes a single chip Multiple Instruction Stream Computer (MISC) capable of extracting instruction level parallelism from a broad spectrum of programs. The MISC architecture uses multiple asynchronous processing elements to separate a program into streams that can be executed in parallel, and integrates a conflict-free message passing system into the lowest level of the processor design to facilitate low latency intra-MISC communication. This approach allows for increased machine parallelism with minimal code expansion, and provides an alternative approach to single instruction stream multi-issue machines such as SuperScalar and VLIW.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Rule Learning
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1-hop neighbor's text information: Extensions of Fill\'s algorithm for perfect simulation. : Fill's algorithm for perfect simulation for attractive finite state space models, unbiased for user impatience, is presented in terms of stochastic recursive sequences and extended in two ways. Repulsive discrete Markov random fields with two coding sets like the auto-Poisson distribution on a lattice with 4-neighbourhood can be treated as monotone systems if a particular partial ordering and quasi-maximal and quasi-minimal states are used. Fill's algorithm then applies directly. Combining Fill's rejection sampling with sandwiching leads to a version of the algorithm, which works for general discrete conditionally specified repulsive models. Extensions to other types of models are briefly discussed.
Target text information: PERFECT SIMULATION OF CONDITIONALLY SPECIFIED MODELS: We discuss how the ideas of producing perfect simulations based on coupling from the past for finite state space models naturally extend to mul-tivariate distributions with infinite or uncountable state spaces such as auto-gamma, auto-Poisson and auto-negative-binomial models, using Gibbs sampling in combination with sandwiching methods originally introduced for perfect simulation of point processes.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Probabilistic Methods
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cora
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1-hop neighbor's text information: "Further facts about input to state stabilization," : Report SYCON-88-15 ABSTRACT Previous results about input to state stabilizability are shown to hold even for systems which are not linear in controls, provided that a more general type of feedback be allowed. Applications to certain stabilization problems and coprime factorizations, as well as comparisons to other results on input to state stability, are also briefly discussed.
Target text information: Reprint of: Sontag, E.D., "Remarks on stabilization and input-to-state stability,":
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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1-hop neighbor's text information: Towards more creative case-based design systems. : Case-based reasoning (CBR) has a great deal to offer in supporting creative design, particularly processes that rely heavily on previous design experience, such as framing the problem and evaluating design alternatives. However, most existing CBR systems are not living up to their potential. They tend to adapt and reuse old solutions in routine ways, producing robust but uninspired results. Little research effort has been directed towards the kinds of situation assessment, evaluation, and assimilation processes that facilitate the exploration of ideas and the elaboration and redefinition of problems that are crucial to creative design. Also, their typically rigid control structures do not facilitate the kinds of strategic control and opportunism inherent in creative reasoning. In this paper, we describe the types of behavior we would like case-based design systems to support, based on a study of designers working on a mechanical engineering problem. We show how the standard CBR framework should be extended and we describe an architecture we are developing to experiment with these ideas. 1
1-hop neighbor's text information: Understanding Creativity: A Case-Based Approach: Dissatisfaction with existing standard case-based reasoning (CBR) systems has prompted us to investigate how we can make these systems more creative and, more broadly, what would it mean for them to be more creative. This paper discusses three research goals: understanding creative processes better, investigating the role of cases and CBR in creative problem solving, and understanding the framework that supports this more interesting kind of case-based reasoning. In addition, it discusses methodological issues in the study of creativity and, in particular, the use of CBR as a research paradigm for exploring creativity.
1-hop neighbor's text information: Opportunistic Reasoning: A Design Perspective. : An essential component of opportunistic behavior is opportunity recognition, the recognition of those conditions that facilitate the pursuit of some suspended goal. Opportunity recognition is a special case of situation assessment, the process of sizing up a novel situation. The ability to recognize opportunities for reinstating suspended problem contexts (one way in which goals manifest themselves in design) is crucial to creative design. In order to deal with real world opportunity recognition, we attribute limited inferential power to relevant suspended goals. We propose that goals suspended in the working memory monitor the internal (hidden) representations of the currently recognized objects. A suspended goal is satisfied when the current internal representation and a suspended goal match. We propose a computational model for working memory and we compare it with other relevant theories of opportunistic planning. This working memory model is implemented as part of our IMPROVISER system.
Target text information: Explaining Serendipitous Recognition in Design, : Creative designers often see solutions to pending design problems in the everyday objects surrounding them. This can often lead to innovation and insight, sometimes revealing new functions and purposes for common design pieces in the process. We are interested in modeling serendipitous recognition of solutions to pending problems in the context of creative mechanical design. This paper characterizes this ability, analyzing observations we have made of it, and placing it in the context of other forms of recognition. We propose a computational model to capture and explore serendipitous recognition which is based on ideas from reconstructive dynamic memory and situation assessment in case-based reasoning.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Case Based
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cora
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1-hop neighbor's text information: Generalized update: Belief change in dynamic settings. : Belief revision and belief update have been proposed as two types of belief change serving different purposes. Belief revision is intended to capture changes of an agent's belief state reflecting new information about a static world. Belief update is intended to capture changes of belief in response to a changing world. We argue that both belief revision and belief update are too restrictive; routine belief change involves elements of both. We present a model for generalized update that allows updates in response to external changes to inform the agent about its prior beliefs. This model of update combines aspects of revision and update, providing a more realistic characterization of belief change. We show that, under certain assumptions, the original update postulates are satisfied. We also demonstrate that plain revision and plain update are special cases of our model, in a way that formally verifies the intuition that revision is suitable for static belief change.
1-hop neighbor's text information: Abduction as belief revision. : We propose a model of abduction based on the revision of the epistemic state of an agent. Explanations must be sufficient to induce belief in the sentence to be explained (for instance, some observation), or ensure its consistency with other beliefs, in a manner that adequately accounts for factual and hypothetical sentences. Our model will generate explanations that nonmonotonically predict an observation, thus generalizing most current accounts, which require some deductive relationship between explanation and observation. It also provides a natural preference ordering on explanations, defined in terms of normality or plausibility. To illustrate the generality of our approach, we reconstruct two of the key paradigms for model-based diagnosis, abductive and consistency-based diagnosis, within our framework. This reconstruction provides an alternative semantics for both and extends these systems to accommodate our predictive explanations and semantic preferences on explanations. It also illustrates how more general information can be incorporated in a principled manner. fl Some parts of this paper appeared in preliminary form as Abduction as Belief Revision: A Model of Preferred Explanations, Proc. of Eleventh National Conf. on Artificial Intelligence (AAAI-93), Washington, DC, pp.642-648 (1993).
1-hop neighbor's text information: Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions. : We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities are automatically extracted form the knowledge base to facilitate the construction and retraction of plausible beliefs. To represent causation, the formalism incorporates the principle of Markov shielding which imposes a stratified set of independence constraints on rankings of interpretations. We show how this formalism resolves some classical problems associated with specificity, prediction and abduction, and how it offers a natural way of unifying belief revision, belief update, and reasoning about actions.
Target text information: An event-based abductive model of update. : The Katsuno and Mendelzon (KM) theory of belief update has been proposed as a reasonable model for revising beliefs about a changing world. However, the semantics of update relies on information which is not readily available. We describe an alternative semantical view of update in which observations are incorporated into a belief set by: a) explaining the observation in terms of a set of plausible events that might have caused that observation; and b) predicting further consequences of those explanations. We also allow the possibility of conditional explanations. We show that this picture naturally induces an update operator conforming to the KM postulates under certain assumptions. However, we argue that these assumptions are not always reasonable, and they restrict our ability to integrate update with other forms of revision when reasoning about action. fl Some parts of this report appeared in preliminary form as An Event-Based Abductive Model of Update, Proc. of Tenth Canadian Conf. on in AI, Banff, Alta., (1994).
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Probabilistic Methods
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cora
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1-hop neighbor's text information: Constructive neural network learning algorithms for multi-category classification. : Constructive learning algorithms offer an approach for incremental construction of potentially near-minimal neural network architectures for pattern classification tasks. Such algorithms help overcome the need for ad-hoc and often inappropriate choice of network topology in the use of algorithms that search for a suitable weight setting in an otherwise a-priori fixed network architecture. Several such algorithms proposed in the literature have been shown to converge to zero classification errors (under certain assumptions) on a finite, non-contradictory training set in a 2-category classification problem. This paper explores multi-category extensions of several constructive neural network learning algorithms for pattern classification. In each case, we establish the convergence to zero classification errors on a multi-category classification task (under certain assumptions). Results of experiments with non-separable multi-category data sets demonstrate the feasibility of this approach to multi-category pattern classification and also suggest several interesting directions for future research.
Target text information: Classification Using -Machines and Constructive Function Approximation: The classification algorithm CLEF combines a version of a linear machine known as a - machine with a non-linear function approxima-tor that constructs its own features. The algorithm finds non-linear decision boundaries by constructing features that are needed to learn the necessary discriminant functions. The CLEF algorithm is proven to separate all consistently labelled training instances, even when they are not linearly separable in the input variables. The algorithm is illustrated on a variety of tasks, showing an improvement over C4.5, a state-of-art de cision tree learning algorithm.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Theory
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cora
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1-hop neighbor's text information: "Application of ESOP Minimization in Machine Learning and Knowledge Discovery," : This paper presents a new application of an Exclusive-Sum-Of-Products (ESOP) minimizer EXORCISM-MV-2: to Machine Learning, and particularly, in Pattern Theory. An analysis of various logic synthesis programs has been conducted at Wright Laboratory for machine learning applications. Creating a robust and efficient Boolean minimizer for machine learning that would minimize a decomposed function cardinality (DFC) measure of functions would help to solve practical problems in application areas that are of interest to the Pattern Theory Group especially those problems that require strongly unspecified multiple-valued-input functions with a large number of variables. For many functions, the complexity minimization of EXORCISM-MV-2 is better than that of Espresso. For small functions, they are worse than those of the Curtis-like Decomposer. However, EXORCISM is much faster, can run on problems with more variables, and significant DFC improvements have also been found. We analyze the cases when EXORCISM is worse than Espresso and propose new improvements for strongly unspecified functions.
1-hop neighbor's text information: Machine learning by function decomposition. : We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and their definitions. This effectively decomposes the problem into smaller, less complex problems. The method is inspired by the Boolean function decomposition approach to the design of digital circuits. To cope with high time complexity of finding an optimal decomposition, we propose a suboptimal heuristic algorithm. The method, implemented in program HINT (HIerarchy Induction Tool), is experimentally evaluated using a set of artificial and real-world learning problems. It is shown that the method performs well both in terms of classification accuracy and discovery of meaningful concept hierarchies.
1-hop neighbor's text information: A dataset decomposition approach to data mining and machine discovery: We present a novel data mining approach based on decomposition. In order to analyze a given dataset, the method decomposes it to a hierarchy of smaller and less complex datasets that can be analyzed independently. The method is experimentally evaluated on a real-world housing loans allocation dataset, showing that the decomposition can (1) discover meaningful intermediate concepts, (2) decompose a relatively complex dataset to datasets that are easy to analyze and comprehend, and (3) derive a classifier of high classification accuracy. We also show that human interaction has a positive effect on both the comprehensibility and classification accuracy.
Target text information: "Pattern Theoretic Learning", : This paper offers a perspective on features and pattern finding in general. This perspective is based on a robust complexity measure called Decomposed Function Car-dinality. A function decomposition algorithm for minimizing this complexity measure and finding the associated features is outlined. Results from experiments with this algorithm are also summarized.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Theory
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cora
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test
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1-hop neighbor's text information: An object-oriented connectionist simulator. : ICSIM is a connectionist net simulator being developed at ICSI and written in Sather. It is object-oriented to meet the requirements for flexibility and reuse of homogeneous and structured connectionist nets and to allow the user to encapsulate efficient customized implementations perhaps running on dedicated hardware. Nets are composed by combining off-the-shelf library classes and if necessary by specializing some of their behaviour. General user interface classes allow a uniform or customized graphic presentation of the nets being modeled.
Target text information: ICSIM: An Object Oriented Simulation Environment for Structured Connectionist Nets. Class Project Report, Physics 250: ICSIM is a simulator for structured connectionism under development at ICSI. Structured connectionism is characterized by the need for flexibility, efficiency and support for the design and reuse of modular substructure. We take the position that a fast object-oriented language like Sather [5] is an appropriate implementation medium to achieve these goals. The core of ICSIM consists of a hierarchy of classes that correspond to simulation entities. New connectionist models are realized by combining and specializing pre-existing classes. Whenever possible, auxillary functionality has been separated out into functional modules in order to keep the basic hierarchy as clean and simple as possible.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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cora
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val
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1-hop neighbor's text information: Neural programming and an internal reinforcement policy. : An important reason for the continued popularity of Artificial Neural Networks (ANNs) in the machine learning community is that the gradient-descent backpropagation procedure gives ANNs a locally optimal change procedure and, in addition, a framework for understanding the ANN learning performance. Genetic programming (GP) is also a successful evolutionary learning technique that provides powerful parameterized primitive constructs. Unlike ANNs, though, GP does not have such a principled procedure for changing parts of the learned system based on its current performance. This paper introduces Neural Programming, a connectionist representation for evolving programs that maintains the benefits of GP. The connectionist model of Neural Programming allows for a regression credit-blame procedure in an evolutionary learning system. We describe a general method for an informed feedback mechanism for Neural Programming, Internal Reinforcement. We introduce an Internal Reinforcement procedure and demon strate its use through an illustrative experiment.
1-hop neighbor's text information: An experimental analysis of schema creation, propagation and disruption in genetic programming. : In this paper we first review the main results in the theory of schemata in Genetic Programming (GP) and summarise a new GP schema theory which is based on a new definition of schema. Then we study the creation, propagation and disruption of this new form of schemata in real runs, for standard crossover, one-point crossover and selection only. Finally, we discuss these results in the light our GP schema theorem.
1-hop neighbor's text information: Using a distance metric on genetic programs to understand genetic operators. : I describe a distance metric called "edit" distance which quantifies the syntactic difference between two genetic programs. In the context of one specific problem, the 6 bit multiplexor, I use the metric to analyze the amount of new material introduced by different crossover operators, the difference among the best individuals of a population and the difference among the best individuals and the rest of the population. The relationships between these data and run performance are imprecise but they are sufficiently interesting to encourage encourage further investigation into the use of edit distance.
Target text information: How Fitness Structure Affects Subsolution Acquisition in Genetic Programming: We define fitness structure in genetic programming to be the mapping between the subprograms of a program and their respective fitness values. This paper shows how various fitness structures of a problem with independent subsolutions relate to the acquisition of sub-solutions. The rate of subsolution acquisition is found to be directly correlated with fitness structure whether that structure is uniform, linear or exponential. An understanding of fitness structure provides partial insight into the complicated relationship between fitness function and the outcome of genetic programming's search.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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3
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Genetic Algorithms
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cora
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val
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1-hop neighbor's text information: Guided crossover: A new operator for genetic algorithm based optimization. : Genetic algorithms (GAs) have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. They have a much better chance of getting to global optima than gradient based methods which usually converge to local sub optima. However, GAs have a tendency of getting only moderately close to the optima in a small number of iterations. To get very close to the optima, the GA needs a very large number of iterations. Whereas gradient based optimizers usually get very close to local optima in a relatively small number of iterations. In this paper we describe a new crossover operator which is designed to endow the GA with gradient-like abilities without actually computing any gradients and without sacrificing global optimality. The operator works by using guidance from all members of the GA population to select a direction for exploration. Empirical results in two engineering design domains and across both binary and floating point representations demonstrate that the operator can significantly improve the steady state error of the GA optimizer.
1-hop neighbor's text information: Genetic Algorithms in Search, Optimization and Machine Learning. : Angeline, P., Saunders, G. and Pollack, J. (1993) An evolutionary algorithm that constructs recurrent neural networks, LAIR Technical Report #93-PA-GNARLY, Submitted to IEEE Transactions on Neural Networks Special Issue on Evolutionary Programming.
1-hop neighbor's text information: "Using case based learning to improve genetic algorithm based design optimization", : In this paper we describe a method for improving genetic-algorithm-based optimization using case-based learning. The idea is to utilize the sequence of points explored during a search to guide further exploration. The proposed method is particularly suitable for continuous spaces with expensive evaluation functions, such as arise in engineering design. Empirical results in two engineering design domains and across different representations demonstrate that the proposed method can significantly improve the efficiency and reliability of the GA optimizer. Moreover, the results suggest that the modification makes the genetic algorithm less sensitive to poor choices of tuning parameters such as muta tion rate.
Target text information: Adaptation of genetic algorithms for engineering design optimization. : Genetic algorithms have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains it was observed that a simple classical implementation of the GA based on binary encoding and bit mutation and crossover was sometimes inefficient and unable to reach the global optimum. Using floating point representation alone does not eliminate the problem. In this paper we describe a way of augmenting the GA with new operators and strategies that take advantage of the structure and properties of such engineering design domains. Empirical results (initially in the domain of conceptual design of supersonic transport aircraft and the domain of high performance supersonic missile inlet design) demonstrate that the newly formulated GA can be significantly better than the classical GA in terms of efficiency and reliability. http://www.cs.rutgers.edu/~shehata/papers.html
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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3
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Genetic Algorithms
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cora
| 1,556
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test
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1-hop neighbor's text information: ,`Subregion-Adaptive Integration of Functions having a Dominant Peak\', :
1-hop neighbor's text information: `Data Reconciliation and Gross Error Detection for Dynamic Systems\', : Gross error detection plays a vital role in parameter estimation and data reconciliation for both dynamic and steady state systems. In particular, recent advances in process optimization now allow data reconciliation of dynamic systems and appropriate problem formulations need to be considered for them. Data errors due to either miscalibrated or faulty sensors or just random events nonrepresentative of the underlying statistical distribution, can induce heavy biases in the parameter estimates and in the reconciled data. In this paper we concentrate on robust estimators and exploratory statistical methods which allow us to detect the gross errors as the data reconciliation is performed. These robust methods have the property of being insensitive to departures from ideal statistical distributions and therefore are insensitive to the presence of outliers. Once the regression is done, the outliers can be detected readily by using exploratory statistical techniques. An important feature for performance of the optimization algorithm and uniqueness of the reconciled data is the ability to classify the variables according to their observability and redundancy properties. Here an observable variable is an unmeasured quantity which can be estimated from the measured variables through the physical model while a nonredundant variable is a measured variable which cannot be estimated other than through its measurements. Variable classification can be used as an aid to design instrumentation schemes. In this
Target text information: Inference in Dynamic Error-in-Variable-Measurement Problems: Efficient algorithms have been developed for estimating model parameters from measured data, even in the presence of gross errors. In addition to point estimates of parameters, however, assessments of uncertainty are needed. Linear approximations provide standard errors, but these can be misleading when applied to models that are substantially nonlinear. To overcome this difficulty, "profiling" methods have been developed for the case in which the regressor variables are error free. In this paper we extend profiling methods to Error-in-Variable-Measurement (EVM) models. We use Laplace's method to integrate out the incidental parameters associated with the measurement errors, and then apply profiling methods to obtain approximate confidence contours for the parameters. This approach is computationally efficient, requiring few function evaluations, and can be applied to large scale problems. It is useful when the certain measurement errors (e.g., input variables) are relatively small, but not so small that they can be ignored.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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1-hop neighbor's text information: (1997) MIMIC: Finding Optima by Estimating Probability Densities, : In many optimization problems, the structure of solutions reflects complex relationships between the different input parameters. For example, experience may tell us that certain parameters are closely related and should not be explored independently. Similarly, experience may establish that a subset of parameters must take on particular values. Any search of the cost landscape should take advantage of these relationships. We present MIMIC, a framework in which we analyze the global structure of the optimization landscape. A novel and efficient algorithm for the estimation of this structure is derived. We use knowledge of this structure to guide a randomized search through the solution space and, in turn, to refine our estimate of the structure. Our technique obtains significant speed gains over other randomized optimization procedures.
Target text information: Reinforcement learning by probability matching. : We present a new algorithm for associative reinforcement learning. The algorithm is based upon the idea of matching a network's output probability with a probability distribution derived from the environment's reward signal. This Probability Matching algorithm is shown to perform faster and be less susceptible to local minima than previously existing algorithms. We use Probability Matching to train mixture of experts networks, an architecture for which other reinforcement learning rules fail to converge reliably on even simple problems. This architecture is particularly well suited for our algorithm as it can compute arbitrarily complex functions yet calculation of the output probability is simple.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Reinforcement Learning
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1-hop neighbor's text information: On Bayesian analysis of mixtures with an unknown number of components. : New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods, that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution. The methodology is applied here to the analysis of univariate normal mixtures, using a hierarchical prior model that offers an approach to dealing with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context.
1-hop neighbor's text information: Bayesian Statistics 4, : The major implementational problem for reversible jump MCMC is that there is commonly no natural way to choose jump proposals since there is no Euclidean structure to guide our choice. In this paper we will consider a mechanism for guiding the proposal choice by analysis of acceptance probabilities for jumps. Essentially the method involves an approximation for the acceptance probability around certain canonical jumps. We will illustrate the procedure using an example of a reversible jump MCMC application, involving a Bayesian analysis of graphical gaussian models.
1-hop neighbor's text information: Modelling risk from a disease in time and space, : This paper combines existing models for longitudinal and spatial data in a hierarchical Bayesian framework, with particular emphasis on the role of time- and space-varying covariate effects. Data analysis is implemented via Markov chain Monte Carlo methods. The methodology is illustrated by a tentative re-analysis of Ohio lung cancer data 1968-88. Two approaches that adjust for unmeasured spatial covariates, particularly tobacco consumption, are described. The first includes random effects in the model to account for unobserved heterogeneity; the second adds a simple urbanization measure as a surrogate for smoking behaviour. The Ohio dataset has been of particular interest because of the suggestion that a nuclear facility in the southwest of the state may have caused increased levels of lung cancer there. However, we contend here that the data are inadequate for a proper investigation of this issue. fl Email: [email protected]
Target text information: Bayesian Detection of Clusters and Discontinuities in Disease Maps:
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Probabilistic Methods
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cora
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1-hop neighbor's text information: Resonance and the perception of musical meter. : Many connectionist approaches to musical expectancy and music composition let the question of What next? overshadow the equally important question of When next?. One cannot escape the latter question, one of temporal structure, when considering the perception of musical meter. We view the perception of metrical structure as a dynamic process where the temporal organization of external musical events synchronizes, or entrains, a listeners internal processing mechanisms. This article introduces a novel connectionist unit, based upon a mathematical model of entrainment, capable of phase and frequency-locking to periodic components of incoming rhythmic patterns. Networks of these units can self-organize temporally structured responses to rhythmic patterns. The resulting network behavior embodies the perception of metrical structure. The article concludes with a discussion of the implications of our approach for theories of metrical structure and musical expectancy.
1-hop neighbor's text information: Induction of multiscale temporal structure. : Learning structure in temporally-extended sequences is a difficult computational problem because only a fraction of the relevant information is available at any instant. Although variants of back propagation can in principle be used to find structure in sequences, in practice they are not sufficiently powerful to discover arbitrary contingencies, especially those spanning long temporal intervals or involving high order statistics. For example, in designing a connectionist network for music composition, we have encountered the problem that the net is able to learn musical structure that occurs locally in time|e.g., relations among notes within a musical phrase|but not structure that occurs over longer time periods|e.g., relations among phrases. To address this problem, we require a means of constructing a reduced description of the sequence that makes global aspects more explicit or more readily detectable. I propose to achieve this using hidden units that operate with different time constants. Simulation experiments indicate that slower time-scale hidden units are able to pick up global structure, structure that simply can not be learned by standard Many patterns in the world are intrinsically temporal, e.g., speech, music, the unfolding of events. Recurrent neural net architectures have been devised to accommodate time-varying sequences. For example, the architecture shown in Figure 1 can map a sequence of inputs to a sequence of outputs. Learning structure in temporally-extended sequences is a difficult computational problem because the input pattern may not contain all the task-relevant information at any instant. Thus, back propagation.
Target text information: REDUCED MEMORY REPRESENTATIONS FOR MUSIC: We address the problem of musical variation (identification of different musical sequences as variations) and its implications for mental representations of music. According to reductionist theories, listeners judge the structural importance of musical events while forming mental representations. These judgments may result from the production of reduced memory representations that retain only the musical gist. In a study of improvised music performance, pianists produced variations on melodies. Analyses of the musical events retained across variations provided support for the reductionist account of structural importance. A neural network trained to produce reduced memory representations for the same melodies represented structurally important events more efficiently than others. Agreement among the musicians' improvisations, the network model, and music-theoretic predictions suggest that perceived constancy across musical variation is a natural result of a reductionist mechanism for producing memory representations.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Neural Networks
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cora
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1-hop neighbor's text information: Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract): We consider learnability with membership queries in the presence of incomplete information. In the incomplete boundary query model introduced by Blum et al. [7], it is assumed that membership queries on instances near the boundary of the target concept may receive a "don't know" answer. We show that zero-one threshold functions are efficiently learnable in this model. The learning algorithm uses split graphs when the boundary region has radius 1, and their generalization to split hypergraphs (for which we give a split-finding algorithm) when the boundary region has constant radius greater than 1. We use a notion of indistinguishability of concepts that is appropriate for this model.
1-hop neighbor's text information: Learning conjunctions of Horn clauses. :
1-hop neighbor's text information: Learning with queries but incomplete information. : We investigate learning with membership and equivalence queries assuming that the information provided to the learner is incomplete. By incomplete we mean that some of the membership queries may be answered by I don't know. This model is a worst-case version of the incomplete membership query model of Angluin and Slonim. It attempts to model practical learning situations, including an experiment of Lang and Baum that we describe, where the teacher may be unable to answer reliably some queries that are critical for the learning algorithm. We present algorithms to learn monotone k-term DNF with membership queries only, and to learn monotone DNF with membership and equivalence queries. Compared to the complete information case, the query complexity increases by an additive term linear in the number of I don't know answers received. We also observe that the blowup in the number of queries can in general be exponential for both our new model and the incomplete membership model.
Target text information: Learning k-term DNF formulas with an incomplete membership oracle. : We consider the problem of learning k-term DNF formulas using equivalence queries and incomplete membership queries as defined by Angluin and Slonim. We demonstrate that this model can be applied to non-monotone classes. Namely, we describe a polynomial-time algorithm that exactly identifies a k-term DNF formula with a k-term DNF hypothesis using incomplete membership queries and equivalence queries from the class of DNF formulas.
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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Theory
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cora
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1-hop neighbor's text information: GAL : Networks that grow when they learn and shrink when they forget, : Learning when limited to modification of some parameters has a limited scope; the capability to modify the system structure is also needed to get a wider range of the learnable. In the case of artificial neural networks, learning by iterative adjustment of synaptic weights can only succeed if the network designer predefines an appropriate network structure, i.e., number of hidden layers, units, and the size and shape of their receptive and projective fields. This paper advocates the view that the network structure should not, as usually done, be determined by trial-and-error but should be computed by the learning algorithm. Incremental learning algorithms can modify the network structure by addition and/or removal of units and/or links. A survey of current connectionist literature is given on this line of thought. "Grow and Learn" (GAL) is a new algorithm that learns an association at one-shot due to being incremental and using a local representation. During the so-called "sleep" phase, units that were previously stored but which are no longer necessary due to recent modifications are removed to minimize network complexity. The incrementally constructed network can later be finetuned off-line to improve performance. Another method proposed that greatly increases recognition accuracy is to train a number of networks and vote over their responses. The algorithm and its variants are tested on recognition of handwritten numerals and seem promising especially in terms of learning speed. This makes the algorithm attractive for on-line learning tasks, e.g., in robotics. The biological plausibility of incremental learning is also discussed briefly. Earlier part of this work was realized at the Laboratoire de Microinformatique of Ecole Polytechnique Federale de Lausanne and was supported by the Fonds National Suisse de la Recherche Scientifique. Later part was realized at and supported by the International Computer Science Institute. A number of people helped by guiding, stimulating discussions or questions: Subutai Ahmad, Peter Clarke, Jerry Feldman, Christian Jutten, Pierre Marchal, Jean Daniel Nicoud, Steve Omohondro and Leon Personnaz.
1-hop neighbor's text information: Case-based reasoning: Foundational issues, methodological variations, and system approaches. : 10 resources, Alan Schultz for installing a WWW server and providing knowledge on CGI scripts, and John Grefenstette for his comments on an earlier version of this paper.
1-hop neighbor's text information: Incremental induction of decision trees. : Technical Report 94-07 February 7, 1994 (updated April 25, 1994) This paper will appear in Proceedings of the Eleventh International Conference on Machine Learning. Abstract This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called `slewing' is introduced. Finally, a non-incremental method is given for finding a decision tree based on a direct metric of a candidate tree.
Target text information: Integration of Case-Based Reasoning and Neural Networks Approaches for Classification: Several different approaches have been used to describe concepts for supervised learning tasks. In this paper we describe two approaches which are: prototype-based incremental neural networks and case-based reasoning approaches. We show then how we can improve a prototype-based neural network model by storing some specific instances in a CBR memory system. This leads us to propose a co-processing hybrid model for classification. 1
I provide the content of the target node and its neighbors' information. The relation between the target node and its 1-hop neighbors is 'citation'. The 7 categories are:
0: Rule_Learning
1: Neural_Networks
2: Case_Based
3: Genetic_Algorithms
4: Theory
5: Reinforcement_Learning
6: Probabilistic_Methods
Question: Based on the content of the target and neighbors' scientific publications, predict the category ID (0 to 6) for the target node.
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2
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Case Based
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cora
| 290
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test
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