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1951-1967 1968-1972 1973-1974 1975 1976 1977 1978 1979 1980-1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002-Circa 13200000
1990open this document and view contents The Strength of Weak Learnability - Robert E. Schapire
open this document and view contents On the complexity of learning minimum time-bounded Turing machines - K. Ko
open this document and view contents The Problem of Expensive Chunks and its Solution by Restricting Expressiveness - Milind Tambe, Allen Newell and Paul S. Rosenbloom
open this document and view contents A Markovian extension of Valiant's learning model - D. Aldous and U. Vazirani
open this document and view contents Learning context-free grammars from structural data in polynomial time - Yasubumi Sakakibara
open this document and view contents Towards a DNA sequencing theory (learning a string) - M. Li
open this document and view contents Adaptive Stochastic Cellular Automata: Theory - Y. C. Lee, S. Qian, R. D. Jones, C. W. Barnes, G. W. Flake, M. K. O'Rourke, K. Lee, H. H. Chen, G. Z. Sun, Y. Q. Zhang, D. Chen and C. L. Giles
open this document and view contents A formal study of learning via queries - O. Watanabe
open this document and view contents Inductive inference from positive data is powerful - T. Shinohara
open this document and view contents Pattern languages are not learnable - R. E. Schapire
open this document and view contents Learning from Examples in a Single-Layer Neural Network - D. Hansel and H. Sompolinsky
open this document and view contents Finite learning by a team - S. Jain and A. Sharma
open this document and view contents Learning DNF under the uniform distribution in quasi-polynomial time - K. Verbeurgt
open this document and view contents A spectral lower bound technique for the size of decision trees and two level circuits - Y. Brandman, J. Hennessy and A. Orlitsky
open this document and view contents Analysis of an Identification Algorithm Arising in the Adaptive Estimation of Markov Chains - A. Arapostathis and S. I. Marcus
open this document and view contents Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding - David E. Goldberg
open this document and view contents Separating distribution-free and mistake-bound learning models over the Boolean domain - A. Blum
open this document and view contents Errata to Extending - Authorless
open this document and view contents Probably Approximately Correct Learning - D. Haussler
open this document and view contents Proceedings of the First International Workshop on Algorithmic Learning Theory - S. Arikawa and S. Goto and S. Ohsuga and T. Yokomori
open this document and view contents On the inference of approximate programs - Carl H. Smith and Mahendran Velauthapillai
open this document and view contents Robust separations in inductive inference - M. A. Fulk
open this document and view contents Proceedings of the Third Annual Workshop on Computational Learning Theory - Mark A. Fulk and John Case
open this document and view contents Neural Network Design and the Complexity of Learning - J. S. Judd
open this document and view contents Relative information - G. Jumarie
open this document and view contents Learning the Distribution in the Extended PAC Model - N. Cesa-Bianchi
open this document and view contents A Thesis in Inductive Inference - R. Wiehagen
open this document and view contents Exploratory Research in Machine Learning - Thomas G. Dietterich
open this document and view contents Universal Approximation of an Unknown Mapping and Its Derivatives Using Multilayer Feedforward Networks - K. Hornik, M. Stinchcombe and H. White
open this document and view contents Convergence to Nearly Minimal Size Grammars by Vacillating Learning Machines - J. Case, S. Jain and A. Sharma
open this document and view contents Learning time varying concepts - T. Kuh, T. Petsche and R. Rivest
open this document and view contents A mechanical method of successful scientific inquiry - D. N. Osherson, M. Stob and S. Weinstein
open this document and view contents Learning from Examples in Large Neural Networks - H. Sompolinsky, N. Tishby and H. S. Seung
open this document and view contents How to do the Right Thing - P. Maes
open this document and view contents On threshold circuits for parity - R. Paturi and M. E. Saks
open this document and view contents Prudence and Other Conditions on Formal Language Learning - M. A. Fulk
open this document and view contents Inference of a rule by a neural network with thermal noise - G. Gyorgyi
open this document and view contents A Note on Polynomial-Time Inference of k-Variable Pattern Language - S. Lange
open this document and view contents Inferring graphs from walks - J. A. Aslam and R. L. Rivest
open this document and view contents Learning in artificial neural networks: a statistical perspective - H. White
open this document and view contents Extending Domain Theories: Two Case Studies in Student Modeling - D. Sleeman, H. Hirsh, I. Ellery and In-Yung Kim
open this document and view contents Some problems of learning with an oracle - E. B. Kinber
open this document and view contents Learning Nested Differences of Intersection Closed Concept Classes - David Helmbold, Robert Sloan and Manfred K. Warmuth
open this document and view contents Connectionist Nonparametric Regression: Multilayer Feedforward Networks can Learn Arbitrary Mappings - H. White
open this document and view contents Negative results for equivalence queries - D. Angluin
open this document and view contents Learning Logical Definitions from Relations - J. R. Quinlan
open this document and view contents A survey of computational learning theory - P. Laird
open this document and view contents The Perceptron Algorithm is Fast for Nonmalicious Distributions - E. B. Baum
open this document and view contents A result of Vapnik with applications - M. Anthony and J. Shawe-Taylor
open this document and view contents Learning switch configurations - V. Raghavan and S. R. Schach
open this document and view contents Exploring an Unknown Graph - X. Deng and C. H. Papadimitriou
open this document and view contents Feature Extraction Using an Unsupervised Neural Network - N. Intrator
open this document and view contents Statistical Theory of Learning a Rule - G'eza Györgi and Naftali Tishby
open this document and view contents A Statistical Approach to Learning and Generalization in Layered Neural Networks - E. Levin, N. Tishby and S. A. Solla
open this document and view contents Program Size Restrictions in Inductive Learning - S. Jain and A. Sharma
open this document and view contents Aggregating Strategies - V. Vovk
open this document and view contents Machine Learning Research at MIT - R. L. Rivest and P. Winston
open this document and view contents The learnability of formal concepts - M. Anthony, N. Biggs and J. Shawe-Taylor
open this document and view contents A guided tour of Chernov bounds - T. Hagerup and C. Rub
open this document and view contents A Necessary Condition for Learning from Positive Examples - Haim Shvaytser
open this document and view contents Empirical Learning as a Function of Concept Character - Larry Rendell and Howard Cho
open this document and view contents Boolean Feature Discovery in Empirical Learning - Giulia Pagallo and David Haussler
open this document and view contents On the sample complextity of PAC-learning using random and chosen examples - B. Eisenberg and R. L. Rivest
open this document and view contents On the sample complexity of finding good search strategies - P. Orponen and R. Greiner
open this document and view contents Learning in the Presence of Additional Information and Inaccurate Information - S. Jain
open this document and view contents Polynomial-time inference of all valid implications for Horn and related formulae - E. Boros, Y. Crama and P. L. Hammer
open this document and view contents Advice to Machine Learning Authors - Pat Langley
open this document and view contents The Mathematical Foundations of Learning Machines - N. J. Nilsson
open this document and view contents Language Acquisition - S. Pinker
open this document and view contents Adaptive Stochastic Cellular Automata: Theory - S. Qian, Y. C. Lee, R. D. Jones, C. W. Barnes, G. W. Flake, M. K. O'Rourke, K. Lee, H. H. Chen, G. Z. Sun, Y. Q. Zhang, D. Chen and C. L. Giles
open this document and view contents Acquiring Recursive and Iterative Concepts with Explanation-Based Learning - Jude W. Shavlik
open this document and view contents On the necessity of Occam algorithms - L. Pitt and R. Board
open this document and view contents Identifying mu-formula decision trees with queries - T. R. Hancock
open this document and view contents Empirical Learning Using Rule Threshold Optimization for Detection of Events in Synthetic Images - David J. Montana
open this document and view contents Program Size and Teams for Computational Learning - A. Sharma
open this document and view contents Learning via queries with teams and anomalies - E. B. Kinber, W. I. Gasarch, T. Zeugmann, M. K. Pleszkoch and C. H. Smith
open this document and view contents A Theory of Learning Classification Rules - W. L. Buntine
open this document and view contents Machine Learning: An Artificial Intelligence Approach - Yves Kodratoff and Ryszard Michalski
open this document and view contents Learning String Patterns and Tree Patterns from Examples - K. Ko, A. Marron and W. Tzeng
open this document and view contents A New Approach to Unsupervised Learning in Deterministic Environments (reprint) - R. L. Rivest and R. E. Schapire
open this document and view contents Polynomial time algorithms for learning neural nets - E. B. Baum
open this document and view contents What Connectionist Models Learn: Learning and Representation in Connectionist Networks - S. J. Hanson and D. J. Burr
open this document and view contents Learning functions of k terms - A. Blum and M. Singh
open this document and view contents Learning Sequential Decision Rules Using Simulation Models and Competition - John J. Grefenstette, Connie Loggia Ramsey and Alan C. Schultz
open this document and view contents Inductive Inference of Optimal Programs: A Survey and Open Problems - T. Zeugmann
open this document and view contents Polynomial time learnability of simple deterministic languages - Hiroki Ishizaka
open this document and view contents Predicting the Future: A Connectionist Approach - A. Weigend, B. Huberman and D. Rumelhart
open this document and view contents Machine Learning: Paradigms and Methods - Jaime Carbonell
open this document and view contents On the number of examples and stages needed for learning decision trees - H. U. Simon
open this document and view contents Separating PAC and Mistake-Bound Learning Models over the Boolean Domain - A. Blum
open this document and view contents The Cascade-Correlation Learning Architecture - S. E. Fahlman and C. Lebiere
open this document and view contents Inductive identification of pattern languages with restricted substitutions - K. Wright
open this document and view contents Introduction to Algorithms - T. H. Cormen, C. E. Leiserson and R. L. Rivest
open this document and view contents CSM: A Computational Model of Cumulative Learning - Hayong Harry Zhou
open this document and view contents Introduction: Special Issue on Computational Learning Theory - Leonard Pitt
open this document and view contents On the complexity of learning from counterexamples and membership queries - W. Maass and G. Turán
open this document and view contents Errata - No Author
open this document and view contents A polynomial time algorithm that learns two hidden net units - E. Baum
open this document and view contents Inductive inference of minimal programs - R. Freivalds
Februaryopen this document and view contents Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks - T. Poggio and F. Girosi
Marchopen this document and view contents Neurogammon: A Neural-Network Backgammon Program - G. Tesauro
open this document and view contents Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks - R. A. Jacobs, M. A. Jordan and A. G. Barto
open this document and view contents On Learning a Union of Half Spaces - E. B. Baum
open this document and view contents Hypothesis Formation and Language Acquisition with an Infinitely Often Correct Teacher - S. Jain and A. Sharma
Aprilopen this document and view contents Extensions of a Theory of Networks for Approximation and Learning: dimensionality and reduction and clustering - T. Poggio and F. Girosi
open this document and view contents Learning via Fourier Transform - Y. Mansour
Juneopen this document and view contents Efficient Methods for Massively Parallel Symbolic Induction: Algorithms and Implementation - R. H. Lathrop
open this document and view contents Inference of LISP Programs from Examples - R. T. Adams
Julyopen this document and view contents Learning to Coordinate Behaviors - P. Maes and R. A. Brooks
open this document and view contents On the Design of Networks with Hidden Variables - R. Dechter
open this document and view contents Language Learning by a Team - S. Jain and A. Sharma
Augustopen this document and view contents Self-improving reactive agents: case studies of Reinforcement Learning Frameworks - L. Lin
open this document and view contents An Efficient Robust Algorithm for Learning Decision Lists - Y. Sakakibara
Septemberopen this document and view contents A Theory of Networks for Approximation and Learning - T. Poggio and F. Girosi
open this document and view contents Learning Binary Relations, Total Orders, and Read-Once Formulas - S. Goldman
Octoberopen this document and view contents Anomalous Learning Helps Succinctness - J. Case, S. Jain and A. Sharma
open this document and view contents Special Issue on Genetic Algorithms - K. D. Jong
open this document and view contents A Formal Theory of Inductive Causation - J. Pearl and T. S. Verma
Novemberopen this document and view contents Learning Stochastic Feedforward Networks - R. M. Neal
Decemberopen this document and view contents Automatic Programming of Behavior-Based Robots using Reinforcement Learning - S. Mahadevan and J. Connell
open this document and view contents Prediction Preserving Reducibility - L. Pitt and M. K. Warmuth