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|---|---|---|---|---|
1,294 | 3 | Title: Representation Requirements for Supporting Decision Model Formulation
Abstract: This paper outlines a methodology for analyzing the representational support for knowledge-based decision-modeling in a broad domain. A relevant set of inference patterns and knowledge types are identified. By comparing the analysi... | [
915
] | Train |
1,295 | 2 | Title: On the Computational Utility of Consciousness
Abstract: We propose a computational framework for understanding and modeling human consciousness. This framework integrates many existing theoretical perspectives, yet is sufficiently concrete to allow simulation experiments. We do not attempt to explain qualia (s... | [
886
] | Train |
1,296 | 6 | Title: Sifting informative examples from a random source.
Abstract: We discuss two types of algorithms for selecting relevant examples that have been developed in the context of computation learning theory. The examples are selected out of a stream of examples that are generated independently at random. The first two... | [
109,
456,
1198
] | Validation |
1,297 | 5 | Title: The Origins of Inductive Logic Programming: A Prehistoric Tale
Abstract: This paper traces the development of the main ideas that have led to the present state of knowledge in Inductive Logic Programming. The story begins with research in psychology on the subject of human concept learning. Results from this r... | [
1174
] | Test |
1,298 | 2 | Title: Rule Revision with Recurrent Neural Networks
Abstract: Recurrent neural networks readily process, recognize and generate temporal sequences. By encoding grammatical strings as temporal sequences, recurrent neural networks can be trained to behave like deterministic sequential finite-state automata. Algorithms ... | [
407,
409,
1285,
1592
] | Test |
1,299 | 1 | Title: Multi-parent Recombination
Abstract: In this section we survey recombination operators that can apply more than two parents to create offspring. Some multi-parent recombination operators are defined for a fixed number of parents, e.g. have arity three, in some operators the number of parents is a random number... | [
714,
1216,
1218,
1392
] | Train |
1,300 | 2 | Title: Tempering Backpropagation Networks: Not All Weights are Created Equal approach yields hitherto unparalleled performance on
Abstract: Backpropagation learning algorithms typically collapse the network's structure into a single vector of weight parameters to be optimized. We suggest that their performance may be i... | [
1320,
1342
] | Train |
1,301 | 5 | Title: Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules
Abstract: This paper addresses a class of learning problems that require a construction of descriptions that combine both M-of-N rules and traditional Disjunctive Normal form (DNF) rules. The prese... | [
960,
1266,
1595,
2346
] | Train |
1,302 | 0 | Title: Correcting for Length Biasing in Conversational Case Scoring
Abstract: Inference's conversational case-based reasoning (CCBR) approach, embedded in the CBR Content Navigator line of products, is susceptible to a bias in its case scoring algorithm. In particular, shorter cases tend to be given higher scores, as... | [
983
] | Train |
1,303 | 1 | Title: Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms
Abstract: We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address four problems to which GA... | [
163,
343,
1274,
2202,
2347
] | Train |
1,304 | 0 | Title: Concept Sharing: A Means to Improve Multi-Concept Learning
Abstract: This paper describes several means for sharing between related concepts to improve learning in the same domain. The sharing comes in the form of substructures or possibly entire structures of previous concepts which may aid in learning other ... | [
1354
] | Test |
1,305 | 1 | Title: Distribution Category: A Parallel Genetic Algorithm for the Set Partitioning Problem
Abstract: This work was supported by the Office of Scientific Computing, U.S. Department of Energy, under Contract W-31-109-Eng-38. It was submitted in partial fulfillment of the requirements for the degree of Doctor of Philo... | [
163,
728,
1016,
1065,
1098,
1106,
1274,
1575,
1740
] | Test |
1,306 | 6 | Title: Improving the Accuracy and Speed of Support Vector Machines
Abstract: Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inversion for ill-posed problems. Against this very general backdrop, any methods for improving the generalization per... | [
607,
1050,
1310,
1499
] | Test |
1,307 | 2 | Title: Extracting Tree-Structured Representations of Trained Networks
Abstract: A significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. We present a novel algorithm, Trepan, for extracting comprehensible, symbolic representations from trained neural n... | [
1057,
1657
] | Validation |
1,308 | 3 | Title: A Defect in Dempster-Shafer Theory
Abstract: By analyzing the relationships among chance, weight of evidence and degree of belief, it is shown that the assertion "chances are special cases of belief functions" and the assertion "Dempster's rule can be used to combine belief functions based on distinct bodies o... | [
1503,
1504,
1506,
1507
] | Test |
1,309 | 6 | Title: A Formalization of Explanation-Based Macro-operator Learning
Abstract: 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 form... | [
924,
1132,
1186
] | Validation |
1,310 | 6 | Title: Incorporating Invariances in Support Vector Learning Machines
Abstract: Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification ... | [
1306,
1499
] | Train |
1,311 | 1 | Title: ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS
Abstract: This paper reports on recent results using genetic algorithms to learn decision rules for complex robot behaviors. The method involves evaluating hypothetical rule sets on a simulator and applying simulated evolution to evolve more effective rules. Th... | [
764,
910,
1140,
1253
] | Validation |
1,312 | 5 | Title: Learning Trees and Rules with Set-valued Features
Abstract: In most learning systems examples are represented as fixed-length "feature vectors", the components of which are either real numbers or nominal values. We propose an extension of the feature-vector representation that allows the value of a feature to ... | [
344,
418,
638,
1260,
1269,
1428,
1622
] | Train |
1,313 | 2 | Title: TRAINREC: A System for Training Feedforward Simple Recurrent Networks Efficiently and Correctly
Abstract: In most learning systems examples are represented as fixed-length "feature vectors", the components of which are either real numbers or nominal values. We propose an extension of the feature-vector represe... | [
1005,
1382,
1623,
1655
] | Train |
1,314 | 4 | Title: Quick 'n' Dirty Generalization For Mobile Robot Learning Content Areas: robotics, reinforcement learning, machine learning,
Abstract: The mobile robot domain challenges policy-iteration reinforcement learning algorithms with difficult problems of structural credit assignment and uncertainty. Structural credit as... | [
1529
] | Test |
1,315 | 2 | Title: Modeling Volatility using State Space Models
Abstract: In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, empirical v... | [
611,
668,
1079,
2452,
2595
] | Train |
1,316 | 4 | Title: KnightCap: A chess program that learns by combining TD() with minimax search
Abstract: In this paper we present TDLeaf(), a variation on the TD() algorithm that enables it to be used in conjunction with minimax search. We present some experiments in which our chess program, KnightCap, used TDLeaf() to learn it... | [
295,
565,
882
] | Test |
1,317 | 0 | Title: Use of Analogy in Automated Theorem Proving
Abstract: Technical Report ATP-90, Artificial Intelligence Laboratory, University of Texas at Austin. | [
1354
] | Train |
1,318 | 2 | Title: Misclassification Minimization
Abstract: The problem of minimizing the number of misclassified points by a plane, attempting to separate two point sets with intersecting convex hulls in n-dimensional real space, is formulated as a linear program with equilibrium constraints (LPEC). This general LPEC can be con... | [
142,
227,
427,
1283
] | Validation |
1,319 | 1 | Title: The Ecology of Echo Echo is a generic ecosystem model in which evolving agents are
Abstract: The problem of minimizing the number of misclassified points by a plane, attempting to separate two point sets with intersecting convex hulls in n-dimensional real space, is formulated as a linear program with equilibriu... | [
1391
] | Train |
1,320 | 2 | Title: On Centering Neural Network Weight Updates
Abstract: Technical Report IDSIA-19-97 Abstract. It has long been known that neural networks can learn faster when their input and hidden unit activities are centered about zero; recently we have extended this approach to also encompass the centering of error signals ... | [
359,
808,
1300
] | Validation |
1,321 | 2 | Title: Priority ASOCS ASOCS models have two significant advantages over other learning models:
Abstract: 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... | [
297,
814,
1041,
1080,
1190,
1222
] | Validation |
1,322 | 6 | Title: Theory and Applications of Agnostic PAC-Learning with Small Decision Trees
Abstract: 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 lear... | [
323,
1020,
1027,
1622,
2539
] | Train |
1,323 | 2 | Title: On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance
Abstract: 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 Resea... | [
1062,
1145,
1149,
1150,
1195
] | Train |
1,324 | 3 | Title: [6] D. Geiger. Graphoids: a qualitative framework for probabilistic inference. An introduction to algorithms for
Abstract: 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... | [
260,
1543,
1747,
2076
] | Validation |
1,325 | 1 | Title: Environmental Effects on Minimal Behaviors in the Minimat World
Abstract: The structure of an environment affects the behaviors of the organisms that have evolved in it. How is that structure to be described, and how can its behavioral consequences be explained and predicted? We aim to establish initial answer... | [
219,
1175,
2170,
2309
] | Train |
1,326 | 3 | Title: Causal diagrams for empirical research
Abstract: The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in whic... | [
105,
248,
419,
776,
971,
1602,
1747,
2144,
2161,
2434
] | Train |
1,327 | 5 | Title: On Biases in Estimating Multi-Valued Attributes
Abstract: We analyse the biases of eleven measures for estimating the quality of the multi-valued attributes. The values of information gain, J-measure, gini-index, and relevance tend to linearly increase with the number of values of an attribute. The values of g... | [
638,
1165,
1569
] | Train |
1,328 | 2 | Title: A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features
Abstract: In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use stand... | [
783,
785,
927,
947,
1020,
1031,
1101,
1107,
1109,
1111,
1155,
1173,
1412,
1423,
1513,
1568,
1584,
1644
] | Train |
1,329 | 6 | Title: Supervised and Unsupervised Discretization of Continuous Features
Abstract: Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify defining characteristics of the methods, and conduct an empirical evalu... | [
1020,
1049,
1071,
1986,
2127
] | Test |
1,330 | 1 | Title: Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work
Abstract: 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 computatio... | [
793,
1167
] | Train |
1,331 | 1 | Title: Mechanisms of Emergent Computation in Cellular Automata
Abstract: 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 e... | [
1167
] | Test |
1,332 | 1 | Title: Statistical Dynamics of the Royal Road Genetic Algorithm
Abstract: 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 mut... | [
1167
] | Train |
1,333 | 1 | Title: Using Genetic Algorithms for Supervised Concept Learning
Abstract: Genetic Algorithms (GAs) have traditionally been used for non-symbolic learning tasks. In this chapter we consider the application of a GA to a symbolic learning task, supervised concept learning from examples. A GA concept learner (GABL) is im... | [
163,
578,
793,
1136,
1207,
1224,
1225,
1369,
1467,
1514,
1708
] | Test |
1,334 | 1 | Title: THE OPTIONS DESIGN EXPLORATION SYSTEM Reference Manual and User Guide Version B2.1
Abstract: Genetic Algorithms (GAs) have traditionally been used for non-symbolic learning tasks. In this chapter we consider the application of a GA to a symbolic learning task, supervised concept learning from examples. A GA co... | [
163,
793,
1130,
1696
] | Train |
1,335 | 3 | Title: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Abstract: We review accuracy estimation methods and compare the two most common methods: cross-validation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown... | [
885,
944,
1024,
1032,
1223,
1267,
1270,
1337,
1339,
1478,
1512,
1607
] | Validation |
1,336 | 3 | Title: Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid
Abstract: Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classification tasks even when the conditional independence assumption on which they are based is violated. However, most studies were ... | [
1027,
1478
] | Validation |
1,337 | 6 | Title: MLC A Machine Learning Library in C
Abstract: We present MLC ++ , a library of C ++ classes and tools for supervised Machine Learning. While MLC ++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can ... | [
944,
1020,
1335,
2300,
2343
] | Test |
1,338 | 3 | Title: Computing Nonparametric Hierarchical Models
Abstract: Bayesian models involving Dirichlet process mixtures are at the heart of the modern nonparametric Bayesian movement. Much of the rapid development of these models in the last decade has been a direct result of advances in simulation-based computational meth... | [
784,
855,
917,
1015,
1654
] | Train |
1,339 | 6 | Title: An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms
Abstract: In this paper we present an average-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monoto... | [
434,
1111,
1335,
1570,
1678,
2443,
2677
] | Train |
1,340 | 2 | Title: ADAPTIVE REGULARIZATION
Abstract: Regularization, e.g., in the form of weight decay, is important for training and optimization of neural network architectures. In this work we provide a tool based on asymptotic sampling theory, for iterative estimation of weight decay parameters. The basic idea is to do a gra... | [
157,
427,
1075
] | Validation |
1,341 | 2 | Title: Growing Layers of Perceptrons: Introducing the Extentron Algorithm
Abstract: vations of perceptrons: (1) when the perceptron learning algorithm cycles among hyperplanes, the hyperplanes may be compared to select one that gives a best split of the examples, and (2) it is always possible for the perceptron to bu... | [
812,
1044
] | Train |
1,342 | 2 | Title: Centering Neural Network Gradient Factors
Abstract: Technical Report IDSIA-19-97 Abstract. It has long been known that neural networks can learn faster when their input and hidden unit activities are centered about zero; recently we have extended this approach to also encompass the centering of error signals [... | [
359,
808,
1300,
2454
] | Validation |
1,343 | 6 | Title: Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle
Abstract: We introduce a new fault-tolerant model of algorithmic learning using an equivalence oracle and an incomplete membership oracle, in which the answers to a random subset of the learner's membership queries may be mi... | [
672,
1003,
1004,
1363,
1364,
1456
] | Train |
1,344 | 0 | Title: Discovery of Physical Principles from Design Experiences
Abstract: One method for making analogies is to access and instantiate abstract domain principles, and one method for acquiring knowledge of abstract principles is to discover them from experience. We view generalization over experiences in the absence o... | [
1046,
1047,
1121,
1138,
1345
] | Validation |
1,345 | 0 | Title: Use of Mental Models for Constraining Index Learning in Experience-Based Design
Abstract: The power of the case-based method comes from the ability to retrieve the "right" case when a new problem is specified. This implies that learning the "right" indices to a case before storing it for potential reuse is cru... | [
1046,
1047,
1344,
1640
] | Test |
1,346 | 2 | Title: References Linear Controller Design, Limits of Performance, "The parallel projection operators of a nonlinear feedback
Abstract: 13] Yang, Y., H.J. Sussmann, and E.D. Sontag, "Stabilization of linear systems with bounded controls," in Proc. Nonlinear Control Systems Design Symp., Bordeaux, June 1992 (M. Fliess, ... | [
1272,
1281,
1451
] | Test |
1,347 | 3 | Title: Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models
Abstract: The Bayesian approach to comparing models involves calculating the posterior probability of each plausible model. For high-dimensional contingency tables, the set of plausible models is very large. We focus ... | [
84,
1147,
1240,
1241
] | Train |
1,348 | 0 | Title: Learning Indices for Schema Selection
Abstract: 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... | [
612,
1047,
1535,
1537
] | Test |
1,349 | 4 | Title: Robust performance and adaptation using receding horizon H 1 control of time varying systems.
Abstract: In this paper we construct suboptimal H 1 controllers which satisfy a new robust performance condition, using the receding horizon technique. A method is described for the synthesis of H 1 controllers online... | [
1217
] | Train |
1,350 | 0 | Title: IGLUE An Instance-based Learning System over Lattice Theory
Abstract: Concept learning is one of the most studied areas in machine learning. A lot of work in this domain deals with decision trees. In this paper, we are concerned with a different kind of technique based on Galois lattices or concept lattices. W... | [
1151
] | Test |
1,351 | 1 | Title: Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification
Abstract: This paper introduces a hybrid learning methodology that integrates genetic algorithms (GAs) and decision tree learning (ID3) in order to evolve optimal subsets of discriminatory features for robust pattern classif... | [
900,
1498
] | Validation |
1,352 | 2 | Title: FONN: Combining First Order Logic with Connectionist Learning
Abstract: This paper presents a neural network architecture that can manage structured data and refine knowledge bases expressed in a first order logic language. The presented framework is well suited to classification problems in which concept de s... | [
611,
1672,
2674
] | Test |
1,353 | 1 | Title: Culling Teaching -1 Culling and Teaching in Neuro-evolution
Abstract: The evolving population of neural nets contains information not only in terms of genes, but also in the collection of behaviors of the population members. Such information can be thought of as a kind of culture of the population. Two ways of... | [
294,
934,
2302,
2317
] | Test |
1,354 | 0 | Title: The Structure-Mapping Engine: Algorithm and Examples
Abstract: 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 consi... | [
75,
313,
479,
806,
911,
992,
994,
1001,
1039,
1040,
1047,
1089,
1123,
1176,
1188,
1304,
1317,
1420,
1426,
1465,
1674,
1680,
1695
] | Train |
1,355 | 0 | Title: Modeling Invention by Analogy in ACT-R
Abstract: 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 clai... | [
1047,
1148,
1640,
1648
] | Train |
1,356 | 2 | Title: Constraint Tangent Distance for On-line Character Recognition
Abstract: In on-line character recognition we can observe two kinds of intra-class variations: small geometric deformations and completely different writing styles. We propose a new approach to deal with these problems by defining an extension of ta... | [
667,
1430
] | Train |
1,357 | 3 | Title: Decimatable Boltzmann Machines vs. Gibbs Sampling
Abstract: Exact Boltzmann learning can be done in certain restricted networks by the technique of decimation. We have enlarged the set of dec-imatable Boltzmann machines by introducing a new and more general decimation rule. We have compared solutions of a prob... | [
1461,
1511
] | Train |
1,358 | 6 | Title: On the Complexity of Function Learning
Abstract: The majority of results in computational learning theory are concerned with concept learning, i.e. with the special case of function learning for classes of functions with range f0; 1g. Much less is known about the theory of learning functions with a larger rang... | [
453,
591,
1567,
1661
] | Train |
1,359 | 2 | Title: Extracting Comprehensible Concept Representations from Trained Neural Networks
Abstract: Although they are applicable to a wide array of problems, and have demonstrated good performance on a number of difficult, real-world tasks, neural networks are not usually applied to problems in which comprehensibility of... | [
1057
] | Train |
1,360 | 6 | Title: Learning From a Consistently Ignorant Teacher
Abstract: One view of computational learning theory is that of a learner acquiring the knowledge of a teacher. We introduce a formal model of learning capturing the idea that teachers may have gaps in their knowledge. The goal of the learner is still to acquire the... | [
1095
] | Validation |
1,361 | 6 | Title: An Efficient Method To Estimate Bagging's Generalization Error
Abstract: In bagging [Bre94a] one uses bootstrap replicates of the training set [Efr79, ET93] to try to improve a learning algorithm's performance. The computational requirements for estimating the resultant generalization error on a test set by me... | [
1463
] | Train |
1,362 | 1 | Title: Towards Automatic Discovery of Building Blocks in Genetic Programming
Abstract: This paper presents an algorithm for the discovery of building blocks in genetic programming (GP) called adaptive representation through learning (ARL). The central idea of ARL is the adaptation of the problem representation, by ex... | [
163,
1178,
1184,
2175
] | Train |
1,363 | 6 | Title: Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions
Abstract: In this paper we describe a new technique for exactly identifying certain classes of read-once Boolean formulas. The method is based on sampling the input-output behavior of the target formula on a probability di... | [
640,
672,
786,
1343,
1364,
2168,
2475,
2653
] | Train |
1,364 | 6 | Title: Learning k-term DNF Formulas with an Incomplete Membership Oracle
Abstract: 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 des... | [
1003,
1004,
1343,
1363,
1469,
1705
] | Validation |
1,365 | 2 | Title: Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING NEURAL NETWORKS WITH LOCALIST PROPERTIES
Abstract: Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and typically suffer from a number of shortcomings as a result. Variations of ANNs that use dynamic topologies have shown ability... | [
809,
812,
814,
1044
] | Validation |
1,366 | 2 | Title: ``Learning Local Error Bars for Nonlinear Regression.'' Learning Local Error Bars for Nonlinear Regression
Abstract: We present a new method for obtaining local error bars for nonlinear regression, i.e., estimates of the confidence in predicted values that depend on the input. We approach this problem by appl... | [
1373,
2239,
2373,
2374,
2413,
2414,
2513,
2562
] | Train |
1,367 | 0 | Title: Learning to Refine Indexing by Introspective Reasoning
Abstract: A significant problem for case-based reasoning (CBR) systems is deciding what features to use in judging case similarity for retrieval. We describe research that addresses the feature selection problem by using introspective reasoning to learn ne... | [
817
] | Train |
1,368 | 0 | Title: Structure oriented case retrieval
Abstract: | [
454,
991
] | Train |
1,369 | 1 | Title: STRUCTURAL LEARNING OF FUZZY RULES FROM NOISED EXAMPLES
Abstract: Inductive learning algorithms try to obtain the knowledge of a system from a set of examples. One of the most difficult problems in machine learning consists in getting the structure of this knowledge. We propose an algorithm able to manage with... | [
1333
] | Test |
1,370 | 5 | Title: From Theory Refinement to KB Maintenance: a Position Statement
Abstract: Since we consider theory refinement (TR) as a possible key concept for a methodologically clear view of knowledge-base maintenance, we try to give a structured overview about the actual state-of-the-art in TR. This overview is arranged al... | [
136,
1102,
2692
] | Train |
1,371 | 1 | Title: Self-Nonself Discrimination in a Computer
Abstract: The problem of protecting computer systems can be viewed generally as the problem of learning to distinguish self from other. We describe a method for change detection which is based on the generation of T cells in the immune system. Mathematical analysis rev... | [
1114
] | Train |
1,372 | 3 | Title: MCMC CONVERGENCE DIAGNOSTIC VIA THE CENTRAL LIMIT THEOREM
Abstract: Markov Chain Monte Carlo (MCMC) methods, as introduced by Gelfand and Smith (1990), provide a simulation based strategy for statistical inference. The application fields related to these methods, as well as theoretical convergence properties, ... | [
352,
896,
904
] | Train |
1,373 | 2 | Title: Direct Multi-Step Time Series Prediction Using TD()
Abstract: This paper explores the application of Temporal Difference (TD) learning (Sutton, 1988) to forecasting the behavior of dynamical systems with real-valued outputs (as opposed to game-like situations). The performance of TD learning in comparison to s... | [
565,
1366,
1718
] | Train |
1,374 | 2 | Title: A simple algorithm that discovers efficient perceptual codes
Abstract: We describe the "wake-sleep" algorithm that allows a multilayer, unsupervised, neural network to build a hierarchy of representations of sensory input. The network has bottom-up "recognition" connections that are used to convert sensory inp... | [
869,
1548
] | Train |
1,375 | 3 | Title: Priors for Infinite Networks
Abstract: Technical Report CRG-TR-94-1 Department of Computer Science University of Toronto 10 King's College Road Toronto, Canada M5S 1A4 Abstract Bayesian inference begins with a prior distribution for model parameters that is meant to capture prior beliefs about the relationship... | [
157,
560,
1452
] | Validation |
1,376 | 4 | Title: Near-Optimal Performance for Reinforcement Learning in Polynomial Time
Abstract: We present new algorithms for reinforcement learning and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decision processes. After observing that the number of acti... | [
306,
565,
738,
1546,
1727
] | Train |
1,377 | 0 | Title: The case for cases: a call for purity in case-based reasoning inherently more difficult than
Abstract: A basic premise of case-based reasoning (CBR) is that it involves reasoning from cases, which are representations of real episodes, rather than from rules, which are facts and if then structures with no stated ... | [
288,
313,
1642
] | Train |
1,378 | 4 | Title: Generalization in Reinforcement Learning: Safely Approximating the Value Function
Abstract: To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to the curse of dimensionality in reinforcem... | [
21,
82,
173,
239,
559,
565,
575,
882,
970,
1440,
1540,
2485
] | Train |
1,379 | 1 | Title: Modeling Simple Genetic Algorithms for Permutation Problems
Abstract: 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 remappin... | [
163,
1611
] | Train |
1,380 | 1 | Title: Evaluating Evolutionary Algorithms
Abstract: 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... | [
163,
793,
1113,
1611,
1717
] | Train |
1,381 | 3 | Title: A Context-Sensitive Generalization of ICA
Abstract: Source separation arises in a surprising number of signal processing applications, from speech recognition to EEG analysis. In the square linear blind source separation problem without time delays, one must find an unmixing matrix which can detangle the resul... | [
570,
576,
1524
] | Test |
1,382 | 2 | Title: AN ADAPTIVE NEURAL NETWORK PARSER
Abstract: We inv estigate the applicability of an adaptive neural network to problems with time-dependent input by demonstrating that a deterministic parser for natural language inputs of significant syntactic complexity can be developed using recurrent connectionist architect... | [
1285,
1313
] | Validation |
1,383 | 2 | Title: Data-defined Problems and Multiversion Neural-net Systems
Abstract: We inv estigate the applicability of an adaptive neural network to problems with time-dependent input by demonstrating that a deterministic parser for natural language inputs of significant syntactic complexity can be developed using recurrent... | [
152,
1384,
1398
] | Validation |
1,384 | 2 | Title: Use of Methodological Diversity to Improve Neural Network Generalisation
Abstract: Littlewood and Miller [1989] present a statistical framework for dealing with coincident failures in multiversion software systems. They develop a theoretical model that holds the promise of high system reliability through the u... | [
1383
] | Test |
1,385 | 0 | Title: Learning Control Knowledge in Models of Expertise ECML'95 Workshop on Knowledge-Level Modelling and Machine Learning
Abstract: During the development and the life-cycle of knowledge-based systems the requirements on the system and the knowledge in the system will change. One of the types of knowledge affected ... | [
1653,
1706
] | Train |
1,386 | 6 | Title: New Evidence Driven State Merging Algorithm
Abstract: Results of the Abbadingo One DFA Learning Competition Abstract This paper first describes the structure and results of the Abbadingo One DFA Learning Competition. The competition was designed to encourage work on algorithms that scale wellboth to larger DFA... | [
672,
1715,
2360
] | Train |
1,387 | 2 | Title: Cortical activity flips among quasi stationary states
Abstract: M. Abeles, H. Bergman and E. Vaadia, School of Medicine and Center for Neural Computation Hebrew University, POB 12272, Jerusalem 91120, Is-rael. E. Seidemann and I. Meilijson, School of Mathematical Sciences, Raymond and Beverly Sackler Faculty o... | [
1239
] | Train |
1,388 | 6 | Title: A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
Abstract: 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... | [
848,
1025,
1027,
1586
] | Train |
1,389 | 2 | Title: Support Vector Machines: Training and Applications
Abstract: The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Laboratories [3, 6, 8, 24]. This new learning algorithm can be seen as an alternative training technique for Polynomi... | [
821,
1050,
1079,
2707
] | Test |
1,390 | 6 | Title: Learning Finite Automata Using Local Distinguishing Experiments
Abstract: One of the open problems listed in [ Rivest and Schapire, 1989 ] is whether and how that the copies of L fl in their algorithm can be combined into one for better performance. This paper describes an algorithm called D fl that does that ... | [
1491
] | Train |
1,391 | 1 | Title: Models in Evolutionary Ecology and the Validation Problem Caswell for their guidance and support. evolutionary
Abstract: All models of natural systems represent an abstraction and simplification of that system. Thus all models suffer a validation problem. Should we believe that the results of the model have any ... | [
1319
] | Validation |
1,392 | 1 | Title: Adapting Crossover in Evolutionary Algorithms
Abstract: One of the issues in evolutionary algorithms (EAs) is the relative importance of two search operators: mutation and crossover. Genetic algorithms (GAs) and genetic programming (GP) stress the role of crossover, while evolutionary programming (EP) and evol... | [
1299
] | Test |
1,393 | 3 | Title: Probabilistic Independence Networks for Hidden Markov Probability Models
Abstract: Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas including statistics, statistical physics, artificial intelligence, speech recognition, image processing, ... | [
905,
976,
1097,
1128,
1397,
1414,
1437,
1502
] | Train |
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