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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
1988open this document and view contents Summary of the panel discussion - D. Angluin, L. Birnbaum, J. Feldman, R. Rivest and L. Valiant
open this document and view contents Supervised Learning of Probability Distributions by Neural Networks - E. Baum and F. Wilczek
open this document and view contents Learning complicated concepts reliably and usefully - R. L. Rivest and R. Sloan
open this document and view contents Functionality in Neural Nets (at AAAI) - L. Valiant
open this document and view contents Learning probabilistic prediction functions - A. DeSantis, G. Markowsky and M. N. Wegman
open this document and view contents Probabilistic Versus Deterministic Inductive Inference in Nonstandard Numberings - R. Freivalds, E. B. Kinber and R. Wiehagen
open this document and view contents Accelerated Learning in Layered Neural Networks - S. A. Solla, E. Levin and M. Fleisher
open this document and view contents Learning by Failing to Explain: Using Partial Explanations to Learn in Incomplete or Intractable Domains - Robert J. Hall
open this document and view contents Learning with hints - D. Angluin
open this document and view contents Criteria for Polynomial-Time (Conceptual) Clustering - Leonard Pitt and Robert E. Reinke
open this document and view contents Linear manifolds are learnable from positive examples - H. Shvaytser
open this document and view contents Sparse Distributed Memory - P. Kanerva
open this document and view contents Learning by Making Models - P. Laird
open this document and view contents Space Efficient Learning Algorithms - D. Haussler
open this document and view contents Scaling relationships in back-propagation learning - G. Tesauro and B. Janssens
open this document and view contents Functionality in neural networks - L. G. Valiant
open this document and view contents Learning from noisy examples - D. Angluin and P. Laird
open this document and view contents Machine Learning as an Experimental Science - P. Langley
open this document and view contents A Tale of Two Classifier Systems - George G. Robertson and Rick L. Riolo
open this document and view contents Identifying languages from stochastic examples - D. Angluin
open this document and view contents A Learning Algorithm for Linear Operators - J. Mycielski
open this document and view contents Learning in threshold networks - P. Raghavan
open this document and view contents Classifier Systems that Learn Internal World Models - Lashon B. Booker
open this document and view contents Learning from Good and Bad Data - Philip D. Laird
open this document and view contents Quantifying Inductive Bias: AI Learning Algorithms and Valiant's Learning Framework - D. Haussler
open this document and view contents Learning to Predict by the Methods of Temporal Differences - Richard S. Sutton
open this document and view contents On the Learnability of DNF Formulae - L. Kucera, A. Marchetti-Spaccamela and M. Protasi
open this document and view contents Strategies for Teaching Layered Networks Classification Tasks - B. S. Wittner and J. S. Denker
open this document and view contents New Theoretical Directions in Machine Learning - D. Haussler
open this document and view contents Non-learnable classes of Boolean formulae that are closed under variable permutation - H. Shvaytser
open this document and view contents Learning regular languages from counterexamples - O. H. Ibarra and T. Jiang
open this document and view contents Computational limitations on learning from examples - Leonard Pitt and Leslie G. Valiant
open this document and view contents Learning with Genetic Algorithms: An Overview - Kenneth De Jong
open this document and view contents Some remarks about space-complexity of learning, and circuit complexity of recognizing - S. Boucheron and J. Sallantin
open this document and view contents Genetic Algorithms and Machine Learning - D. E. Goldberg and J. H. Holland
open this document and view contents Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets - R. P. Gorman and T. J. Sejnowski
open this document and view contents Prudence in Language Learning - S. A. Kurtz and J. S. Royer
open this document and view contents Inductive Syntactical Synthesis of Programs From Sample Computations - E. B. Kinber
open this document and view contents Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises - J. L. McClelland and D. E. Rumelhart
open this document and view contents Inductive inference: an abstract approach - J. C. Cherniavsky, M. Velauthapillai and R. Statman
open this document and view contents Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm - N. Littlestone
open this document and view contents Learning pattern languages from a single initial example and from queries - A. Marron
open this document and view contents Mathematical/Mechanical? Learners pay a price for Bayesianism - D. N. Osherson, M. Stob and S. Weinstein
open this document and view contents On the Power of Recursive Optimizers - Thomas Zeugmann
open this document and view contents Saving the Phenomenon: Requirements that Inductive Inference Machines not Contradict Known Data - M. A. Fulk
open this document and view contents Learning Boolean Formulae or Finite Automata is as Hard as Factoring - M. Kearns and L. G. Valiant
open this document and view contents Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms - John J. Grefenstette
open this document and view contents Types of noise in data for concept learning - R. Sloan
open this document and view contents Proceedings of the First Annual Workshop on Computational Learning Theory - D. Haussler and L. Pitt
open this document and view contents Learning and Programming in Classifier Systems - Richard K. Belew and Stephanie Forrest
open this document and view contents Grammatical inference for even linear languages based on control sets - Yuji Takada
open this document and view contents A Non-Iterative Maximum Entropy Algorithm - S. A. Goldman and R. L. Rivest
open this document and view contents Transformation of probabilistic learning strategies into deterministic learning strategies - R. Daley
open this document and view contents Learnability by fixed distributions - G. M. Benedek and A. Itai
open this document and view contents Requests for hints that return no hints - D. Angluin
open this document and view contents Synthesising Inductive Expertise - Daniel N. Osherson, Michael Stob and Scott Weinstein
open this document and view contents The power of vacillation - J. Case
open this document and view contents Extending the Valiant learning model - J. Amsterdam
open this document and view contents Learning theories in a subset of a polyadic logic - R. B. Banerji
open this document and view contents Learning k-DNF with Noise in the Attributes - G. Shackelford and D. Volper
open this document and view contents On the learnability of finite automata - M. Li and U. Vazirani
open this document and view contents On Rationality and Learning - J. Doyle
open this document and view contents Efficient unsupervised learning - P. D. Laird
open this document and view contents Probability and Plurality for Aggregations of Learning Machines - L. Pitt and C. Smith
open this document and view contents Genetic Algorithms in Noisy Environments - J. Michael Fitzpatrick and John J. Grefenstette
open this document and view contents Learning in neural networks - S. Judd
open this document and view contents Neurocomputing: Foundations of Research - J. A. Anderson and E. Rosenfeld
open this document and view contents A Review of Machine Learning at AAAI-87 - R. Greiner, B. Silver, S. Becker and M. Grüninger
Januaryopen this document and view contents The Valiant Learning Model: Extensions and Assessment - J. Amsterdam
Marchopen this document and view contents Machine Learning: the Human Connection - R. L. Rivest and W. Remmele
open this document and view contents A New Model for Inductive Inference - R. L. Rivest and R. Sloan
open this document and view contents Exploiting Chaos to Predict the Future and Reduce Noise - J. D. Farmer and J. J. Sidorowich
Aprilopen this document and view contents Queries and Concept Learning - D. Angluin
Mayopen this document and view contents Supervised learning and systems with excess degrees of freedom - M. I. Jordan
open this document and view contents Diversity-Based Inference of Finite Automata - R. E. Schapire
Juneopen this document and view contents Two New Frameworks for Learning - B. K. Natarajan and P. Tadepalli
Julyopen this document and view contents Nonuniform Learnability - G. M. Benedek and A. Itai
open this document and view contents Automatic Pattern Recognition: A Study of the Probability of Error - L. Devroye
Augustopen this document and view contents Some Philosophical Problems with Formal Learning Theory - J. Amsterdam
open this document and view contents A Pattern Classification Approach to Evaluation Function Learning - K. Lee and S. Mahajan
Septemberopen this document and view contents A Study of Scaling and Generalization in Neural Networks - S. Ahmad
Octoberopen this document and view contents Exemplar-based learning: theory and implementation - S. Salzberg
open this document and view contents Learning a Probability Distribution Efficiently and Reliably - P. Laird and E. Gamble
Novemberopen this document and view contents Equivalence Queries and DNF formulas - D. Angluin
Decemberopen this document and view contents Thoughts on Hypothesis Boosting - M. Kearns