node_id int64 0 76.9k | label int64 0 39 | text stringlengths 13 124k | neighbors listlengths 0 3.32k | mask stringclasses 4
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|---|---|---|---|---|
794 | 6 | Title: Learning rules with local exceptions
Abstract: We present a learning algorithm for rule-based concept representations called ripple-down rule sets. Ripple-down rule sets allow us to deal with the exceptions for each rule separately by introducing exception rules, exception rules for each exception rule etc. up... | [
795
] | Train |
795 | 6 | Title: Learning Hierarchical Rule Sets
Abstract: We present an algorithm for learning sets of rules that are organized into up to k levels. Each level can contain an arbitrary number of rules "if c then l" where l is the class associated to the level and c is a concept from a given class of basic concepts. The rules ... | [
794,
865
] | Train |
796 | 2 | Title: Characterizing Carbon Dynamics in a Northern Forest Using SIR-C/X-SAR Imagery Characterizing Carbon Dynamics in a
Abstract: 1 ABSTRACT | [
1629
] | Train |
797 | 2 | Title: Regularities in a Random Mapping from Orthography to Semantics
Abstract: In this paper we investigate representational and methodological issues in a attractor network model of the mapping from orthography to semantics based on [Plaut, 1995]. We find that, contrary to psycholinguistic studies, the response tim... | [
1645
] | Test |
798 | 6 | Title: Composite Geometric Concepts and Polynomial Predictability
Abstract: In this paper we investigate representational and methodological issues in a attractor network model of the mapping from orthography to semantics based on [Plaut, 1995]. We find that, contrary to psycholinguistic studies, the response time to... | [
507,
792,
1095,
1105,
1433
] | Validation |
799 | 0 | Title: A utility-based approach to learning in a mixed Case-Based and Model-Based Reasoning architecture
Abstract: Case-based reasoning (CBR) can be used as a form of "caching" solved problems to speedup later problem solving. Using "cached" cases brings additional costs with it due to retrieval time, case adaptation... | [
1122,
1699,
1706,
2656
] | Train |
800 | 1 | Title: Vector Quantizer Design Using Genetic Algorithms
Abstract: A Genetic Algorithmic (GA) approach to vector quantizer design that combines the conventional Generalized Lloyd Algorithm (GLA) [6] is presented. We refer to this hybrid as the Genetic Generalized Lloyd Algorithm (GGLA). It works briefly as follows: A ... | [
163,
1136
] | Test |
801 | 0 | Title: Massively Parallel Support for Case-based Planning
Abstract: In case-based planning (CBP), previously generated plans are stored as cases in memory and can be reused to solve similar planning problems in the future. CBP can save considerable time over planning from scratch (generative planning), thus offering ... | [
313,
1475,
1542
] | Train |
802 | 3 | Title: Double Censoring: Characterization and Computation of the Nonparametric Maximum Likelihood Estimator
Abstract: In case-based planning (CBP), previously generated plans are stored as cases in memory and can be reused to solve similar planning problems in the future. CBP can save considerable time over planning ... | [
973,
1168
] | Test |
803 | 1 | Title: Optimal and Asymptotically Optimal Equi-partition of Rectangular Domains via Stripe Decomposition
Abstract: We present an efficient method for assigning any number of processors to tasks associated with the cells of a rectangular uniform grid. Load balancing equi-partition constraints are observed while approx... | [
53,
357,
1439,
1563
] | Train |
804 | 4 | Title: Exploration Bonuses and Dual Control
Abstract: Finding the Bayesian balance between exploration and exploitation in adaptive optimal control is in general intractable. This paper shows how to compute suboptimal estimates based on a certainty equivalence approximation arising from a form of dual control. This s... | [
1459,
1585,
1664,
1697
] | Test |
805 | 2 | Title: Critical Points for Least-Squares Problems Involving Certain Analytic Functions, with Applications to Sigmoidal Nets
Abstract: This paper deals with nonlinear least-squares problems involving the fitting to data of parameterized analytic functions. For generic regression data, a general result establishes the ... | [
930,
990
] | Train |
806 | 0 | Title: The Role of Generic Models in Conceptual Change
Abstract: 1 This research was funded in part by NSF Grant No. IRI-92-10925 and in part by ONR Grant No. N00014-92-J-1234. We thank John Clement for the use of his protocol transcript, James Greeno for his contribution to developing our constructive modeling inter... | [
1121,
1138,
1354
] | Train |
807 | 4 | Title: Designing Neural Networks for Adaptive Control
Abstract: This paper discusses the design of neural networks to solve specific problems of adaptive control. In particular, it investigates the influence of typical problems arising in real-world control tasks as well as techniques for their solution that exist in... | [
294,
1438,
1672
] | Test |
808 | 2 | Title: Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain
Abstract: We present the information-theoretic derivation of a learning algorithm that clusters unlabelled data with linear discriminants. In contrast to methods that try to preserve information about the input patterns, ... | [
359,
731,
863,
1320,
1342,
1450
] | Validation |
809 | 2 | Title: A Self-Adjusting Dynamic Logic Module
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, and dynamic control. An ASOCS is an adaptive netw... | [
297,
812,
814,
908,
919,
1044,
1080,
1129,
1190,
1222,
1229,
1365,
1615,
1639
] | Test |
810 | 2 | Title: MIXED MEMORY MARKOV MODELS FOR TIME SERIES ANALYSIS
Abstract: This paper presents a method for analyzing coupled time series using Markov models in a domain where the state space is immense. To make the parameter estimation tractable, the large state space is represented as the Cartesian product of smaller sta... | [
1287,
1718
] | Train |
811 | 1 | Title: ROBOT LEARNING WITH PARALLEL GENETIC ALGORITHMS ON NETWORKED COMPUTERS
Abstract: This work explores the use of machine learning methods for extracting knowledge from simulations of complex systems. In particular, we use genetic algorithms to learn rule-based strategies used by autonomous robots. The evaluation... | [
910,
1140,
1253
] | Train |
812 | 2 | Title: Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS
Abstract: Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and often suffer from a number of shortcomings as a result. Variations of ANNs that use dynamic topologies have shown ab... | [
809,
814,
1044,
1080,
1341,
1365
] | Train |
813 | 1 | Title: The Application of a Parallel Genetic Algorithm to the n=m=P=C max Flowshop Problem
Abstract: Hard combinatorial problems in sequencing and scheduling led recently into further research of genetic algorithms. Canonical coding of the symmetric TSP can be modified into a coding of the n-job m-machine flowshop pr... | [
163,
1523
] | Train |
814 | 2 | Title: A VLSI Implementation of a Parallel, Self-Organizing Learning Model
Abstract: This paper presents a VLSI implementation of the Priority Adaptive Self-Organizing Concurrent System (PASOCS) learning model that is built using a multi-chip module (MCM) substrate. Many current hardware implementations of neural net... | [
809,
812,
1044,
1080,
1129,
1321,
1365
] | Train |
815 | 1 | Title: Genetic Algorithm based Scheduling in a Dynamic Manufacturing Environment
Abstract: The application of adaptive optimization strategies to scheduling in manufacturing systems has recently become a research topic of broad interest. Population based approaches to scheduling predominantly treat static data models... | [
880,
1523
] | Train |
816 | 2 | Title: Comparing Adaptive and Non-Adaptive Connection Pruning With Pure Early Stopping
Abstract: Neural network pruning methods on the level of individual network parameters (e.g. connection weights) can improve generalization, as is shown in this empirical study. However, an open problem in the pruning methods known... | [
881,
1203
] | Test |
817 | 0 | Title: Case-Based Similarity Assessment: Estimating Adaptability from Experience
Abstract: Case-based problem-solving systems rely on similarity assessment to select stored cases whose solutions are easily adaptable to fit current problems. However, widely-used similarity assessment strategies, such as evaluation of ... | [
818,
819,
1125,
1212,
1367
] | Train |
818 | 0 | Title: Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning
Abstract: The case-based reasoning process depends on multiple overlapping knowledge sources, each of which provides an opportunity for learning. Exploiting these opportunities requires not only determining the learning mechanisms to use... | [
582,
817,
819,
1125,
1212,
1215
] | Train |
819 | 0 | Title: A Case Study of Case-Based CBR
Abstract: Case-based reasoning depends on multiple knowledge sources beyond the case library, including knowledge about case adaptation and criteria for similarity assessment. Because hand coding this knowledge accounts for a large part of the knowledge acquisition burden for dev... | [
817,
818,
1154,
1212,
1215
] | Validation |
820 | 2 | Title: NESTED NETWORKS FOR ROBOT CONTROL
Abstract: Case-based reasoning depends on multiple knowledge sources beyond the case library, including knowledge about case adaptation and criteria for similarity assessment. Because hand coding this knowledge accounts for a large part of the knowledge acquisition burden for ... | [
829,
1115,
1146,
1252
] | Train |
821 | 2 | Title: Massive Data Discrimination via Linear Support Vector Machines
Abstract: 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 magn... | [
607,
1389,
1421
] | Test |
822 | 6 | Title: Achieving High-Accuracy Text-to-Speech with Machine Learning
Abstract: In 1987, Sejnowski and Rosenberg developed their famous NETtalk system for English text-to-speech. This chapter describes a machine learning approach to text-to-speech that builds upon and extends the initial NETtalk work. Among the many ex... | [
1484,
1644
] | Train |
823 | 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
] | Train |
824 | 0 | Title: Merge Strategies for Multiple Case Plan Replay
Abstract: Planning by analogical reasoning is a learning method that consists of the storage, retrieval, and replay of planning episodes. Planning performance improves with the accumulation and reuse of a library of planning cases. Retrieval is driven by domain-de... | [
1215,
1621,
1707
] | Validation |
825 | 0 | Title: Towards Mixed-Initiative Rationale-Supported Planning
Abstract: This paper introduces our work on mixed-initiative, rationale-supported planning. The work centers on the principled reuse and modification of past plans by exploiting their justification structure. The goal is to record as much as possible of the... | [
1215,
1707
] | Train |
826 | 2 | Title: Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks
Abstract: The primary goal of inductive learning is to generalize well that is, induce a function that accurately produces the correct output for future inputs. Hansen and Salamon showed that, under cert... | [
1237,
1273,
1422,
1457,
1606
] | Validation |
827 | 3 | Title: Two Algorithms for Inducing Structural Equation Models from Data
Abstract: We present two algorithms for inducing structural equation models from data. Assuming no latent variables, these models have a causal interpretation and their parameters may be estimated by linear multiple regression. Our algorithms are... | [
909,
913,
1527,
1894
] | Test |
828 | 2 | Title: AN ANYTIME APPROACH TO CONNECTIONIST THEORY REFINEMENT: REFINING THE TOPOLOGIES OF KNOWLEDGE-BASED NEURAL NETWORKS
Abstract: We present two algorithms for inducing structural equation models from data. Assuming no latent variables, these models have a causal interpretation and their parameters may be estimated... | [
1422,
1457
] | Train |
829 | 2 | Title: Approximation with neural networks: Between local and global approximation
Abstract: We investigate neural network based approximation methods. These methods depend on the locality of the basis functions. After discussing local and global basis functions, we propose a a multi-resolution hierarchical method. Th... | [
820,
962,
1252
] | Train |
830 | 2 | Title: Orthogonal incremental learning of a feedforward network
Abstract: Orthogonal incremental learning (OIL) is a new approach of incremental training for a feedforward network with a single hidden layer. OIL is based on the idea to describe the output weights (but not the hidden nodes) as a set of orthogonal basi... | [
1252
] | Test |
831 | 0 | Title: Beyond predictive accuracy: what?
Abstract: Today's potential users of machine learning technology are faced with the non-trivial problem of choosing, from the large, ever-increasing number of available tools, the one most appropriate for their particular task. To assist the often non-initiated users, it is de... | [
885,
2243,
2466
] | Train |
832 | 2 | Title: Learning Continuous Attractors in Recurrent Networks
Abstract: One approach to invariant object recognition employs a recurrent neural network as an associative memory. In the standard depiction of the network's state space, memories of objects are stored as attractive fixed points of the dynamics. I argue for... | [
1052,
1701
] | Train |
833 | 1 | Title: Graph Coloring with Adaptive Evolutionary Algorithms
Abstract: This paper presents the results of an experimental investigation on solving graph coloring problems with Evolutionary Algorithms (EA). After testing different algorithm variants we conclude that the best option is an asexual EA using order-based re... | [
714,
1218,
1516,
1796,
2001
] | Validation |
834 | 2 | Title: Simple Neuron Models for Independent Component Analysis
Abstract: Recently, several neural algorithms have been introduced for Independent Component Analysis. Here we approach the problem from the point of view of a single neuron. First, simple Hebbian-like learning rules are introduced for estimating one of t... | [
576,
1067,
1520,
1814,
1821
] | Test |
835 | 0 | Title: Case-Based Acquisition of Place Knowledge
Abstract: In this paper we define the task of place learning and describe one approach to this problem. The framework represents distinct places using evidence grids, a probabilistic description of occupancy. Place recognition relies on case-based classification, augme... | [
688,
1248
] | Test |
836 | 6 | Title: Unsupervised Constructive Learning
Abstract: In constructive induction (CI), the learner's problem representation is modified as a normal part of the learning process. This is useful when the initial representation is inadequate or inappropriate. In this paper, I argue that the distinction between constructive... | [
375,
426,
1266,
1595
] | Train |
837 | 5 | Title: Inductive Database Design
Abstract: When designing a (deductive) database, the designer has to decide for each predicate (or relation) whether it should be defined extensionally or intensionally, and what the definition should look like. An intelligent system is presented to assist the designer in this task. I... | [
1007,
1489,
1686
] | Test |
838 | 3 | Title: Possible World Partition Sequences: A Unifying Framework for Uncertain Reasoning
Abstract: When we work with information from multiple sources, the formalism each employs to handle uncertainty may not be uniform. In order to be able to combine these knowledge bases of different formats, we need to first establ... | [
1458
] | Train |
839 | 2 | Title: Signal Separation by Nonlinear Hebbian Learning
Abstract: When we work with information from multiple sources, the formalism each employs to handle uncertainty may not be uniform. In order to be able to combine these knowledge bases of different formats, we need to first establish a common basis for characteri... | [
59,
354,
576,
874,
1067,
1072,
1520,
1526,
1709
] | Train |
840 | 2 | Title: Using the Grow-And-Prune Network to Solve Problems of Large Dimensionality
Abstract: This paper investigates a technique for creating sparsely connected feed-forward neural networks which may be capable of producing networks that have very large input and output layers. The architecture appears to be particula... | [
1196
] | Train |
841 | 3 | Title: Bayesian inference for nondecomposable graphical Gaussian models
Abstract: In this paper we propose a method to calculate the posterior probability of a nondecomposable graphical Gaussian model. Our proposal is based on a new device to sample from Wishart distributions, conditional on the graphical constraints... | [
1240,
2559
] | Train |
842 | 4 | Title: Metrics for Temporal Difference Learning
Abstract: For an absorbing Markov chain with a reinforcement on each transition, Bertsekas (1995a) gives a simple example where the function learned by TD( ll ) depends on ll . Bertsekas showed that for ll =1 the approximation is optimal with respect to a least-squares ... | [
565,
1540
] | Train |
843 | 4 | Title: Locally Weighted Learning for Control
Abstract: Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been ... | [
568,
575,
1101,
2658
] | Test |
844 | 1 | Title: Evolving Compact Solutions in Genetic Programming: A Case Study
Abstract: Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Sym... | [
55,
934,
974,
1184,
1784
] | Validation |
845 | 2 | Title: Mixture Models in the Exploration of Structure-Activity Relationships in Drug Design
Abstract: We report on a study of mixture modeling problems arising in the assessment of chemical structure-activity relationships in drug design and discovery. Pharmaceutical research laboratories developing test compounds fo... | [
784
] | Test |
846 | 1 | Title: Evolving Visually Guided Robots
Abstract: 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. | [
219,
757,
781,
1533,
2173,
2204
] | Validation |
847 | 6 | Title: A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with
Abstract: We give an analysis of the generalization error of cross validation in terms of two natural measures of the difficulty of the problem under consideration: the approximation rate (the accuracy to which the target... | [
710,
848,
967,
1032,
1402,
1468
] | Train |
848 | 6 | Title: An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the
Abstract: We investigate the problem of model selection in the setting of supervised learning of boolean functions from independent random examples. More precisely, we compare methods for finding a balan... | [
591,
847,
967,
1032,
1223,
1388,
1400,
1468,
1607,
1739
] | Test |
849 | 5 | Title: Generalization of Clauses under Implication
Abstract: In the area of inductive learning, generalization is a main operation, and the usual definition of induction is based on logical implication. Recently there has been a rising interest in clausal representation of knowledge in machine learning. Almost all in... | [
1720
] | Validation |
850 | 3 | Title: COMPUTING DISTRIBUTIONS OF ORDER STATISTICS
Abstract: Recurrence relationships among the distribution functions of order statistics of independent, but not identically distributed, random quantities are derived. These results extend known theory and provide computationally practicable algorithms for a variety ... | [
917
] | Train |
851 | 3 | Title: Bayesian Probability Theory A General Method for Machine Learning
Abstract: This paper argues that Bayesian probability theory is a general method for machine learning. From two well-founded axioms, the theory is capable of accomplishing learning tasks that are incremental or non-incremental, supervised or uns... | [
1491
] | Test |
852 | 3 | Title: Bayesian Models for Non-Linear Autoregressions
Abstract: This paper argues that Bayesian probability theory is a general method for machine learning. From two well-founded axioms, the theory is capable of accomplishing learning tasks that are incremental or non-incremental, supervised or unsupervised. It can l... | [
1015,
1654
] | Train |
853 | 6 | Title: Error-Correcting Output Codes for Local Learners
Abstract: Error-correcting output codes (ECOCs) represent classes with a set of output bits, where each bit encodes a binary classification task corresponding to a unique partition of the classes. Algorithms that use ECOCs learn the function corresponding to eac... | [
1019,
1053,
1732,
2423
] | Validation |
854 | 1 | Title: A Comparison of Random Search versus Genetic Programming as Engines for Collective Adaptation
Abstract: We have integrated the distributed search of genetic programming (GP) based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as proble... | [
1178,
1231,
2211,
2598
] | Test |
855 | 3 | Title: Hierarchical priors and mixture models, with application in regression and density estimation
Abstract: We have integrated the distributed search of genetic programming (GP) based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as proble... | [
1338,
1654
] | Train |
856 | 1 | Title: Hierarchical priors and mixture models, with application in regression and density estimation
Abstract: A Genetic Algorithm Tutorial Darrell Whitley Technical Report CS-93-103 (Revised) November 10, 1993 | [
163,
793,
1016,
1153
] | Train |
857 | 0 | Title: How to Retrieve Relevant Information?
Abstract: The document presents an approach to judging relevance of retrieved information based on a novel approach to similarity assessment. Contrary to other systems, we define relevance measures (context in similarity) at query time. This is necessary if since without a... | [
1125,
1483,
2060
] | Validation |
858 | 0 | Title: MULTISTRATEGY LEARNING IN REACTIVE CONTROL SYSTEMS FOR AUTONOMOUS ROBOTIC NAVIGATION
Abstract: 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 co... | [
281,
566,
991,
1084,
2035,
2303,
2556
] | Validation |
859 | 6 | Title: On-Site Learning
Abstract: A model for on-site learning is presented. The system learns by querying "hard" patterns while classifying "easy" ones. This model is related to query-based filtering methods, but takes into account that in addition to labelling, filtering through the data has a cost. A few simple po... | [
1198
] | Test |
860 | 1 | Title: A Study in Program Response and the Negative Effects of Introns in Genetic Programming
Abstract: The standard method of obtaining a response in tree-based genetic programming is to take the value returned by the root node. In non-tree representations, alternate methods have been explored. One alternative is to... | [
934,
1184,
1911,
1940
] | Test |
861 | 6 | Title: In Defense of C4.5: Notes on Learning One-Level Decision Trees
Abstract: We discuss the implications of Holte's recently-published article, which demonstrated that on the most commonly used data very simple classification rules are almost as accurate as decision trees produced by Quinlan's C4.5. We consider, i... | [
1236,
1431,
1678
] | Train |
862 | 0 | Title: Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion
Abstract: We describe an approach to grapheme-to-phoneme conversion which is both language-independent and data-oriented. Given a set of examples (spelling words with their associated phonetic representation) in a language, a grapheme-to-phoneme... | [
638,
785,
986,
1019,
1155,
1407,
1513,
1812,
2364,
2423
] | Train |
863 | 2 | Title: Empirical Entropy Manipulation for Real-World Problems
Abstract: No finite sample is sufficient to determine the density, and therefore the entropy, of a signal directly. Some assumption about either the functional form of the density or about its smoothness is necessary. Both amount to a prior over the space ... | [
576,
808,
2499
] | Train |
864 | 2 | Title: A Sparse Representation for Function Approximation
Abstract: We derive a new general representation for a function as a linear combination of local correlation kernels at optimal sparse locations (and scales) and characterize its relation to PCA, regularization, sparsity principles and Support Vector Machines.... | [
611,
1079
] | Validation |
865 | 6 | Title: ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment
Abstract: A satisficing search problem consists of a set of probabilistic experiments to be performed in some order, without repetitions, until a satisfying configuration of successes and failures has been reache... | [
251,
795,
932,
1505,
2560
] | Test |
866 | 2 | Title: Analyzing Phase Transitions in High-Dimensional Self-Organizing Maps
Abstract: 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 descriptio... | [
18,
890
] | Train |
867 | 2 | Title: Comparison of Neural and Statistical Classifiers| Theory and Practice
Abstract: Research Reports A13 January 1996 | [
46,
74,
667,
1493
] | Validation |
868 | 4 | Title: Adaptive Load Balancing: A Study in Multi-Agent Learning
Abstract: We study the process of multi-agent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study ada... | [
1643,
1649
] | Train |
869 | 3 | Title: Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model
Abstract: Brendan J. Frey and Geoffrey E. Hinton 1997. Efficient stochastic source coding and an application to a Bayesian network source model. The Computer Journal 40, 157-165. In this paper, we introduce a new algorithm... | [
76,
1374
] | Test |
870 | 4 | Title: Learning to take risks
Abstract: Agents that learn about other agents and can exploit this information possess a distinct advantage in competitive situations. Games provide stylized adversarial environments to study agent learning strategies. Researchers have developed game playing programs that learn to play ... | [
523,
1687
] | Validation |
871 | 3 | Title: Factorial Learning and the EM Algorithm
Abstract: Many real world learning problems are best characterized by an interaction of multiple independent causes or factors. Discovering such causal structure from the data is the focus of this paper. Based on Zemel and Hinton's cooperative vector quantizer (CVQ) arch... | [
74,
1591
] | Validation |
872 | 2 | Title: A Blind Identification and Separation Technique via Multi-layer Neural Networks
Abstract: This paper deals with the problem of blind identification and source separation which consists of estimation of the mixing matrix and/or the separation of a mixture of stochastically independent sources without a priori k... | [
59,
570,
1520
] | Test |
873 | 2 | Title: On the performance of orthogonal source separation algorithms
Abstract: Source separation consists in recovering a set of n independent signals from m n observed instantaneous mixtures of these signals, possibly corrupted by additive noise. Many source separation algorithms use second order information in a wh... | [
920,
1520
] | Test |
874 | 2 | Title: LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES
Abstract: In this paper a neural network approach for reconstruction of natural highly correlated images from linear (additive) mixture of them is proposed. A multi-layer architecture with local on-line learning rules is developed to sol... | [
59,
570,
576,
839,
1520
] | Validation |
875 | 4 | Title: LEARNING TO SOLVE MARKOVIAN DECISION PROCESSES
Abstract: In this paper a neural network approach for reconstruction of natural highly correlated images from linear (additive) mixture of them is proposed. A multi-layer architecture with local on-line learning rules is developed to solve the problem of blind sep... | [
370,
554,
775,
933,
1192
] | Train |
876 | 6 | Title: Using and combining predictors that specialize
Abstract: We study online learning algorithms that predict by combining the predictions of several subordinate prediction algorithms, sometimes called experts. These simple algorithms belong to the multiplicative weights family of algorithms. The performance of th... | [
453,
1006,
1025,
1269
] | Train |
877 | 5 | Title: Naive Bayesian classifier within ILP-R
Abstract: When dealing with the classification problems, current ILP systems often lag behind state-of-the-art attributional learners. Part of the blame can be ascribed to a much larger hypothesis space which, therefore, cannot be as thoroughly explored. However, sometime... | [
1010,
1569,
1578,
1651
] | Validation |
878 | 2 | Title: Nonsmooth Dynamic Simulation With Linear Programming Based Methods
Abstract: Process simulation has emerged as a valuable tool for process design, analysis and operation. In this work, we extend the capabilities of iterated linear programming (LP) for dealing with problems encountered in dynamic nonsmooth proc... | [
1023,
1472
] | Train |
879 | 4 | Title: A GENERAL METHOD FOR INCREMENTAL SELF-IMPROVEMENT AND MULTI-AGENT LEARNING
Abstract: Process simulation has emerged as a valuable tool for process design, analysis and operation. In this work, we extend the capabilities of iterated linear programming (LP) for dealing with problems encountered in dynamic nonsmo... | [
700,
979
] | Validation |
880 | 1 | Title: Control of Parallel Population Dynamics by Social-Like Behavior of GA-Individuals
Abstract: A frequently observed difficulty in the application of genetic algorithms to the domain of optimization arises from premature convergence. In order to preserve genotype diversity we develop a new model of auto-adaptive ... | [
815,
1523
] | Train |
881 | 2 | Title: Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules
Abstract: Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar proble... | [
74,
112,
572,
816,
1119,
1203,
1256,
1411,
2203,
2295,
2405,
2423,
2454,
2702
] | Train |
882 | 4 | Title: Learning To Play the Game of Chess
Abstract: This paper presents NeuroChess, a program which learns to play chess from the final outcome of games. NeuroChess learns chess board evaluation functions, represented by artificial neural networks. It integrates inductive neural network learning, temporal differencin... | [
523,
565,
575,
1012,
1316,
1378,
1529,
1616
] | Train |
883 | 0 | Title: A Preprocessing Model for Integrating CBR and Prototype-Based Neural Networks
Abstract: Some important factors that play a major role in determining the performances of a CBR (Case-Based Reasoning) system are the complexity and the accuracy of the retrieval phase. Both flat memory and inductive approaches suff... | [
1210,
1665
] | Train |
884 | 6 | Title: PAC Learning of One-Dimensional Patterns
Abstract: Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We consider the problem of PAC-learning the concept class of geometric patterns where the target geometric pattern is a confi... | [
109,
1658
] | Train |
885 | 6 | Title: Model selection using measure functions
Abstract: The concept of measure functions for generalization performance is suggested. This concept provides an alternative way of selecting and evaluating learned models (classifiers). In addition, it makes it possible to state a learning problem as a computational pro... | [
177,
831,
1335
] | Test |
886 | 2 | Title: In Search Of Articulated Attractors
Abstract: Recurrent attractor networks offer many advantages over feed-forward networks for the modeling of psychological phenomena. Their dynamic nature allows them to capture the time course of cognitive processing, and their learned weights may often be easily interpreted... | [
1295
] | Train |
887 | 6 | Title: Simplifying Decision Trees: A Survey
Abstract: Induced decision trees are an extensively-researched solution to classification tasks. For many practical tasks, the trees produced by tree-generation algorithms are not comprehensible to users due to their size and complexity. Although many tree induction algorit... | [
983,
1531
] | Train |
888 | 3 | Title: Looking at Markov Samplers through Cusum Path Plots: a simple diagnostic idea
Abstract: In this paper, we propose to monitor a Markov chain sampler using the cusum path plot of a chosen 1-dimensional summary statistic. We argue that the cusum path plot can bring out, more effectively than the sequential plot, ... | [
41,
896,
1731
] | Test |
889 | 3 | Title: Bounding Convergence Time of the Gibbs Sampler in Bayesian Image Restoration
Abstract: This paper gives precise, easy to compute bounds on the convergence time of the Gibbs sampler used in Bayesian image reconstruction. For sampling from the Gibbs distribution both with and without the presence of an external ... | [
41,
892,
904,
1713,
2153
] | Train |
890 | 2 | Title: Breaking Rotational Symmetry in a Self-Organizing Map-Model for Orientation Map Development
Abstract: This paper gives precise, easy to compute bounds on the convergence time of the Gibbs sampler used in Bayesian image reconstruction. For sampling from the Gibbs distribution both with and without the presence ... | [
18,
866
] | Train |
891 | 3 | Title: Coupled hidden Markov models for modeling interacting processes
Abstract: c flMIT Media Lab Perceptual Computing / Learning and Common Sense Technical Report 405 3nov96, revised 3jun97 Abstract We present methods for coupling hidden Markov models (hmms) to model systems of multiple interacting processes. The r... | [
1437
] | Train |
892 | 3 | Title: Possible biases induced by MCMC convergence diagnostics
Abstract: c flMIT Media Lab Perceptual Computing / Learning and Common Sense Technical Report 405 3nov96, revised 3jun97 Abstract We present methods for coupling hidden Markov models (hmms) to model systems of multiple interacting processes. The resulting... | [
41,
138,
889,
904,
1713,
1716
] | Train |
893 | 5 | Title: LEARNING LOGICAL EXCEPTIONS IN CHESS
Abstract: c flMIT Media Lab Perceptual Computing / Learning and Common Sense Technical Report 405 3nov96, revised 3jun97 Abstract We present methods for coupling hidden Markov models (hmms) to model systems of multiple interacting processes. The resulting models have multip... | [
1174,
1259,
1290
] | Train |
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