1990 | | | **The Strength of Weak Learnability** - *Robert E. Schapire* |

| | | **On the complexity of learning minimum time-bounded Turing machines** - *K. Ko* |

| | | **The Problem of Expensive Chunks and its Solution by Restricting Expressiveness** - *Milind Tambe, Allen Newell and Paul S. Rosenbloom* |

| | | **A Markovian extension of Valiant's learning model** - *D. Aldous and U. Vazirani* |

| | | **Learning context-free grammars from structural data in polynomial time** - *Yasubumi Sakakibara* |

| | | **Towards a DNA sequencing theory (learning a string)** - *M. Li* |

| | | **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* |

| | | **A formal study of learning via queries** - *O. Watanabe* |

| | | **Inductive inference from positive data is powerful** - *T. Shinohara* |

| | | **Pattern languages are not learnable** - *R. E. Schapire* |

| | | **Learning from Examples in a Single-Layer Neural Network** - *D. Hansel and H. Sompolinsky* |

| | | **Finite learning by a team** - *S. Jain and A. Sharma* |

| | | **Learning DNF under the uniform distribution in quasi-polynomial time** - *K. Verbeurgt* |

| | | **A spectral lower bound technique for the size of decision trees and two level circuits** - *Y. Brandman, J. Hennessy and A. Orlitsky* |

| | | **Analysis of an Identification Algorithm Arising in the Adaptive Estimation of Markov Chains** - *A. Arapostathis and S. I. Marcus* |

| | | **Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding** - *David E. Goldberg* |

| | | **Separating distribution-free and mistake-bound learning models over the Boolean domain** - *A. Blum* |

| | | **Errata to Extending** - * Authorless* |

| | | **Probably Approximately Correct Learning** - *D. Haussler* |

| | | **Proceedings of the First International Workshop on Algorithmic Learning Theory** - *S. Arikawa and S. Goto and S. Ohsuga and T. Yokomori* |

| | | **On the inference of approximate programs** - *Carl H. Smith and Mahendran Velauthapillai* |

| | | **Robust separations in inductive inference** - *M. A. Fulk* |

| | | **Proceedings of the Third Annual Workshop on Computational Learning Theory** - *Mark A. Fulk and John Case* |

| | | **Neural Network Design and the Complexity of Learning** - *J. S. Judd* |

| | | **Relative information** - *G. Jumarie* |

| | | **Learning the Distribution in the Extended PAC Model** - *N. Cesa-Bianchi* |

| | | **A Thesis in Inductive Inference** - *R. Wiehagen* |

| | | **Exploratory Research in Machine Learning** - *Thomas G. Dietterich* |

| | | **Universal Approximation of an Unknown Mapping and Its Derivatives Using Multilayer Feedforward Networks** - *K. Hornik, M. Stinchcombe and H. White* |

| | | **Convergence to Nearly Minimal Size Grammars by Vacillating Learning Machines** - *J. Case, S. Jain and A. Sharma* |

| | | **Learning time varying concepts** - *T. Kuh, T. Petsche and R. Rivest* |

| | | **A mechanical method of successful scientific inquiry** - *D. N. Osherson, M. Stob and S. Weinstein* |

| | | **Learning from Examples in Large Neural Networks** - *H. Sompolinsky, N. Tishby and H. S. Seung* |

| | | **How to do the Right Thing** - *P. Maes* |

| | | **On threshold circuits for parity** - *R. Paturi and M. E. Saks* |

| | | **Prudence and Other Conditions on Formal Language Learning** - *M. A. Fulk* |

| | | **Inference of a rule by a neural network with thermal noise** - *G. Gyorgyi* |

| | | **A Note on Polynomial-Time Inference of k-Variable Pattern Language** - *S. Lange* |

| | | **Inferring graphs from walks** - *J. A. Aslam and R. L. Rivest* |

| | | **Learning in artificial neural networks: a statistical perspective** - *H. White* |

| | | **Extending Domain Theories: Two Case Studies in Student Modeling** - *D. Sleeman, H. Hirsh, I. Ellery and In-Yung Kim* |

| | | **Some problems of learning with an oracle** - *E. B. Kinber* |

| | | **Learning Nested Differences of Intersection Closed Concept Classes** - *David Helmbold, Robert Sloan and Manfred K. Warmuth* |

| | | **Connectionist Nonparametric Regression: Multilayer Feedforward Networks can Learn Arbitrary Mappings** - *H. White* |

| | | **Negative results for equivalence queries** - *D. Angluin* |

| | | **Learning Logical Definitions from Relations** - *J. R. Quinlan* |

| | | **A survey of computational learning theory** - *P. Laird* |

| | | **The Perceptron Algorithm is Fast for Nonmalicious Distributions** - *E. B. Baum* |

| | | **A result of Vapnik with applications** - *M. Anthony and J. Shawe-Taylor* |

| | | **Learning switch configurations** - *V. Raghavan and S. R. Schach* |

| | | **Exploring an Unknown Graph** - *X. Deng and C. H. Papadimitriou* |

| | | **Feature Extraction Using an Unsupervised Neural Network** - *N. Intrator* |

| | | **Statistical Theory of Learning a Rule** - *G'eza Györgi and Naftali Tishby* |

| | | **A Statistical Approach to Learning and Generalization in Layered Neural Networks** - *E. Levin, N. Tishby and S. A. Solla* |

| | | **Program Size Restrictions in Inductive Learning** - *S. Jain and A. Sharma* |

| | | **Aggregating Strategies** - *V. Vovk* |

| | | **Machine Learning Research at MIT** - *R. L. Rivest and P. Winston* |

| | | **The learnability of formal concepts** - *M. Anthony, N. Biggs and J. Shawe-Taylor* |

| | | **A guided tour of Chernov bounds** - *T. Hagerup and C. Rub* |

| | | **A Necessary Condition for Learning from Positive Examples** - *Haim Shvaytser* |

| | | **Empirical Learning as a Function of Concept Character** - *Larry Rendell and Howard Cho* |

| | | **Boolean Feature Discovery in Empirical Learning** - *Giulia Pagallo and David Haussler* |

| | | **On the sample complextity of PAC-learning using random and chosen examples** - *B. Eisenberg and R. L. Rivest* |

| | | **On the sample complexity of finding good search strategies** - *P. Orponen and R. Greiner* |

| | | **Learning in the Presence of Additional Information and Inaccurate Information** - *S. Jain* |

| | | **Polynomial-time inference of all valid implications for Horn and related formulae** - *E. Boros, Y. Crama and P. L. Hammer* |

| | | **Advice to Machine Learning Authors** - *Pat Langley* |

| | | **The Mathematical Foundations of Learning Machines** - *N. J. Nilsson* |

| | | **Language Acquisition** - *S. Pinker* |

| | | **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* |

| | | **Acquiring Recursive and Iterative Concepts with Explanation-Based Learning** - *Jude W. Shavlik* |

| | | **On the necessity of Occam algorithms** - *L. Pitt and R. Board* |

| | | **Identifying ***mu*-formula decision trees with queries - *T. R. Hancock* |

| | | **Empirical Learning Using Rule Threshold Optimization for Detection of Events in Synthetic Images** - *David J. Montana* |

| | | **Program Size and Teams for Computational Learning** - *A. Sharma* |

| | | **Learning via queries with teams and anomalies** - *E. B. Kinber, W. I. Gasarch, T. Zeugmann, M. K. Pleszkoch and C. H. Smith* |

| | | **A Theory of Learning Classification Rules** - *W. L. Buntine* |

| | | **Machine Learning: An Artificial Intelligence Approach** - *Yves Kodratoff and Ryszard Michalski* |

| | | **Learning String Patterns and Tree Patterns from Examples** - *K. Ko, A. Marron and W. Tzeng* |

| | | **A New Approach to Unsupervised Learning in Deterministic Environments (reprint)** - *R. L. Rivest and R. E. Schapire* |

| | | **Polynomial time algorithms for learning neural nets** - *E. B. Baum* |

| | | **What Connectionist Models Learn: Learning and Representation in Connectionist Networks** - *S. J. Hanson and D. J. Burr* |

| | | **Learning functions of k terms** - *A. Blum and M. Singh* |

| | | **Learning Sequential Decision Rules Using Simulation Models and Competition** - *John J. Grefenstette, Connie Loggia Ramsey and Alan C. Schultz* |

| | | **Inductive Inference of Optimal Programs: A Survey and Open Problems** - *T. Zeugmann* |

| | | **Polynomial time learnability of simple deterministic languages** - *Hiroki Ishizaka* |

| | | **Predicting the Future: A Connectionist Approach** - *A. Weigend, B. Huberman and D. Rumelhart* |

| | | **Machine Learning: Paradigms and Methods** - *Jaime Carbonell* |

| | | **On the number of examples and stages needed for learning decision trees** - *H. U. Simon* |

| | | **Separating PAC and Mistake-Bound Learning Models over the Boolean Domain** - *A. Blum* |

| | | **The Cascade-Correlation Learning Architecture** - *S. E. Fahlman and C. Lebiere* |

| | | **Inductive identification of pattern languages with restricted substitutions** - *K. Wright* |

| | | **Introduction to Algorithms** - *T. H. Cormen, C. E. Leiserson and R. L. Rivest* |

| | | **CSM: A Computational Model of Cumulative Learning** - *Hayong Harry Zhou* |

| | | **Introduction: Special Issue on Computational Learning Theory** - *Leonard Pitt* |

| | | **On the complexity of learning from counterexamples and membership queries** - *W. Maass and G. Turán* |

| | | **Errata** - *No Author* |

| | | **A polynomial time algorithm that learns two hidden net units** - *E. Baum* |

| | | **Inductive inference of minimal programs** - *R. Freivalds* |

| February | | **Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks** - *T. Poggio and F. Girosi* |

| March | | **Neurogammon: A Neural-Network Backgammon Program** - *G. Tesauro* |

| | | **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* |

| | | **On Learning a Union of Half Spaces** - *E. B. Baum* |

| | | **Hypothesis Formation and Language Acquisition with an Infinitely Often Correct Teacher** - *S. Jain and A. Sharma* |

| April | | **Extensions of a Theory of Networks for Approximation and Learning: dimensionality and reduction and clustering** - *T. Poggio and F. Girosi* |

| | | **Learning via Fourier Transform** - *Y. Mansour* |

| June | | **Efficient Methods for Massively Parallel Symbolic Induction: Algorithms and Implementation** - *R. H. Lathrop* |

| | | **Inference of LISP Programs from Examples** - *R. T. Adams* |

| July | | **Learning to Coordinate Behaviors** - *P. Maes and R. A. Brooks* |

| | | **On the Design of Networks with Hidden Variables** - *R. Dechter* |

| | | **Language Learning by a Team** - *S. Jain and A. Sharma* |

| August | | **Self-improving reactive agents: case studies of Reinforcement Learning Frameworks** - *L. Lin* |

| | | **An Efficient Robust Algorithm for Learning Decision Lists** - *Y. Sakakibara* |

| September | | **A Theory of Networks for Approximation and Learning** - *T. Poggio and F. Girosi* |

| | | **Learning Binary Relations, Total Orders, and Read-Once Formulas** - *S. Goldman* |

| October | | **Anomalous Learning Helps Succinctness** - *J. Case, S. Jain and A. Sharma* |

| | | **Special Issue on Genetic Algorithms** - *K. D. Jong* |

| | | **A Formal Theory of Inductive Causation** - *J. Pearl and T. S. Verma* |

| November | | **Learning Stochastic Feedforward Networks** - *R. M. Neal* |

| December | | **Automatic Programming of Behavior-Based Robots using Reinforcement Learning** - *S. Mahadevan and J. Connell* |

| | | **Prediction Preserving Reducibility** - *L. Pitt and M. K. Warmuth* |