| 1990 | |  | A mechanical method of successful ascientific inquiry - D. N. Osherson, M. Stob and S. Weinstein |
| | | A New Approach to Unsupervised Learning in Deterministic Environments reprint - R. L. Rivest and R. E. Schapire |
| | | Learning DNF under the uniform distribution in quasi-polynomial time - K. Verbeurgt |
| | | Identifying -formula decision trees with queries - T. R. Hancock |
| | | Learning String patterns and Tree Patterns from Examples - K. Ko and Assaf T. W. Marron |
| | | Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding - David E. Goldberg |
| | | On the number of examples and stages needed for learning decision trees - H. U. Simon |
| | | Proc. of the First International Workshop on Algorithmic Learning Theory - S. Arikawa and S. Goto and S. Ohsuga and T. Yokomori |
| | | Convergence to Nearly Minimal Size Grammars by Vacillating Learning Machines - J. Case, S. Jain and A. Sharma |
| | | The Strength of Weak Learnability - Robert E. Schapire |
| | | Robust separations in inductive inference - M. A. Fulk |
| | | On the sample complextity of PAC-learning using random and chosen examples - B. Eisenberg and R. L. Rivest |
| | | The learnability of formal concepts - M. Anthony, N. Biggs and J. Shawe-Taylor |
| | | A survey of computational learning theory - P. Laird |
| | | Proc. 3rd Annu. Workshop on Comput. Learning Theory - M. Fulk and J. Case |
| | | Program Size Restrictions in Inductive Learning - S. Jain and A. Sharma |
| | | Acquiring Recursive and Iterative Concepts with Explanation-Based Learning - Jude W. Shavlik |
| | | Statistical Theory of Learning a Rule - G’eza Györgi and Naftali Tishby |
| | | Connectionist Nonparametric Regression: Multilayer Feedforward Networks can Learn Arbitrary Mappings - H. White |
| | | Pattern languages are not learnable - R. E. Schapire |
| | | Learning in the Presence of Additional Information and Inaccurate Information - S. Jain |
| | | Inductive inference from positive data is powerful - T. Shinohara |
| | | On the complexity of learning from counterexamples and membership queries - W. Maass and G. Turán |
| | | The Mathematical Foundations of Learning Machines - N. J. Nilsson |
| | | Learning from Examples in Large Neural Networks - H. Sompolinsky, N. Tishby and H. S. Seung |
| | | Inference of a rule by a neural network with thermal noise - G. Gyorgyi |
| | | A guided tour of Chernov bounds - T. Hagerup and C. Rub |
| | | Learning functions of k terms - A. Blum and M. Singh |
| | | Feature Extraction Using an Unsupervised Neural Network - N. Intrator |
| | | Learning from Examples in a Single-Layer Neural Network - D. Hansel and H. Sompolinsky |
| | | A Note on Polynomial-Time Inference of k-Variable Pattern Language - S. Lange |
| | | 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 |
| | | A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network Training - L. K. Jones |
| | | Finite learning by a team - S. Jain and A. Sharma |
| | | Neural Network Design and the Complexity of Learning - J. S. Judd |
| | | Separating distribution-free and mistake-bound learning models over the Boolean domain - A. Blum |
| | | A Statistical Approach to Learning and Generalization in Layered Neural Networks - E. Levin, N. Tishby and S. A. Solla |
| | | Relative information - G. Jumarie |
| | | Inductive Inference with Additional Information - M. Fulk |
| | | Monotonic and Nonmonotonic Inductive Inference of Functions and Patterns - K. P. Jantke |
| | | Analysis of an Identification Algorithm Arising in the Adaptive Estimation of Markov Chains - A. Arapostathis and S. I. Marcus |
| | | Learning time varying concepts - T. Kuh, T. Petsche and R. Rivest |
| | | Learning by distances - S. Ben-David, A. Itai and E. Kushilevitz |
| | | Errata to Extending - Authorless |
| | | Advice to Machine Learning Authors - Pat Langley |
| | | Introduction: Special Issue on Computational Learning Theory - Leonard Pitt |
| | | Machine Learning: Paradigms and Methods - J. C. Editor |
| | | A polynomial time algorithm that learns two hidden net units - E. Baum |
| | | Towards a DNA sequencing theory learning a string - M. Li |
| | | Polynomial-time inference of all valid implications for Horn and related formulae - E. Boros, Y. Crama and P. L. Hammer |
| | | A Theory of Learning Classification Rules - W. L. Buntine |
| | | A formal study of learning via queries - O. Watanabe |
| | | How to do the Right Thing - P. Maes |
| | | Prudence and Other Conditions on Formal Language Learning - M. Fulk |
| | | On the complexity of learning minimum time-bounded Turing machines - K. Ko |
| | | On threshold circuits for parity - R. Paturi and M. E. Saks |
| | | Simulation Results for a new two-armed bandit heuristic - R. L. Rivest and Y. Yin |
| | | On the sample complexity of weak learning - S. A. Goldman, M. J. Kearns and R. E. Schapire |
| | | Inductive Inference of Optimal Programs: A Survey and Open Problems - T. Zeugmann |
| | | On the inference of approximate programs - Carl Smith and Mahendra Velauthapillai |
| | | Separating PAC and Mistake-Bound Learning Models over the Boolean Domain - A. Blum |
| | | A result of Vapnik with applications - M. Anthony and J. Shawe-Taylor |
| | | The Perceptron Algorithm is Fast for Nonmalicious Distributions - E. B. Baum |
| | | Exploratory Research in Machine Learning - Thomas G. Dietterich |
| | | CSM: A Computational Model of Cumulative Learning - Hayong Harry Zhou |
| | | Machine Learning Research at MIT - R. L. Rivest and P. Winston |
| | | The Cascade-Correlation Learning Architecture - S. E. Fahlman and C. Lebiere |
| | | Learning in artificial neural networks: a statistical perspective - H. White |
| | | Empirical Learning Using Rule Threshold Optimization for Detection of Events in Synthetic Images - David J. Montana |
| | | Predicting the Future: A Connectionist Approach - A. Weigend, B. Huberman and D. Rumelhart |
| | | Extending Domain Theories: Two Case Studies in Student Modeling - D. Sleeman et al. |
| | | A Necessary Condition for Learning from Positive Examples - Haim Shvaytser |
| | | Negative results for equivalence queries - D. Angluin |
| | | Machine Learning: An Artificial Intelligence Approach - Y. Kodratoff and R. S. Michalski |
| | | Inferring graphs from walks - J. A. Aslam and R. L. Rivest |
| | | A spectral lower bound technique for the size of decision trees and two level circuits - Y. Brandman, J. Hennessy and A. Orlitsky |
| | | Inductive inference of minimal programs - R. Freivalds |
| | | Exploring an Unknown Graph - X. Deng and C. H. Papadimitriou |
| | | What Connectionist Models Learn: Learning and Representation in Connectionist Networks - S. J. Hanson and D. J. Burr |
| | | On the sample complexity of finding good search strategies - P. Orponen and R. Greiner |
| | | Empirical Learning as a Function of Concept Character - Larry Rendell and Howard Cho |
| | | Learning context-free grammars from structural data in polynomial time - Y. Sakakibara |
| | | Polynomial time algorithms for learning neural nets - E. B. Baum |
| | | Some problems of learning with an oracle - E. B. Kinber |
| | | Inductive identification of pattern languages with restricted substitutions - K. Wright |
| | | Learning the Distribution in the Extended PAC Model - N. Cesa-Bianchi |
| | | Probably Approximately Correct Learning - D. Haussler |
| | | Learning via queries with teams and anomalies - E. B. Kinber, W. I. Gasarch, T. Zeugmann, M. K. Pleszkoch and C. H. Smith |
| | | 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 Logical Definitions from Relations - J. R. Quinlan |
| | | Learning switch configurations - V. Raghavan and S. R. Schach |
| | | A Thesis in Inductive Inference - R. Wiehagen |
| | | Aggregating Strategies - V. Vovk |
| | | Universal Approximation of an Unknown Mapping and Its Derivatives Using Multilayer Feedforward Networks - K. Hornik, M. Stinchcombe and H. White |
| | | Introduction to Algorithms - T. H. Cormen, C. E. Leiserson and R. L. Rivest |
| | | On the necessity of Occam algorithms - L. Pitt and R. Board |
| | | Efficient distribution-free learning of probabilistic concepts - M. J. Kearns and R. E. Schapire |
| | | Program Size and Teams for Computational Learning - A. Sharma |
| | | Language Acquisition - S. Pinker |
| | | Boolean Feature Discovery in Empirical Learning - Giulia Pagallo and David Haussler |
| | | Learning Nested Differences of Intersection Closed Concept Classes - D. Helmbold, R. Sloan and M. K. Warmuth |
| | | Learning Sequential Decision Rules Using Simulation Models and Competition - John J. Grefenstette, Connie Loggia Ramsey and Alan C. Schultz |
| | | Polynomial time learnability of simple deterministic languages - H. Ishizaka |
| | | 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 |
| February | | Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks - T. Poggio and F. Girosi |
| March | | 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 |
| | | Neurogammon: A Neural-Network Backgammon Program - G. Tesauro |
| | | 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 | | Language Learning by a Team - S. Jain and A. Sharma |
| | | On the Design of Networks with Hidden Variables - R. Dechter |
| | | Learning to Coordinate Behaviors - P. Maes and R. A. Brooks |
| | | Forward models: Supervised learning with a distal teacher - M. Joardan and D. Rumelhart |
| August | | An Efficient Robust Algorithm for Learning Decision Lists - Y. Sakakibara |
| | | Self-improving reactive agents: case studies of Reinforcement Learning Frameworks - L. Lin |
| 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 | | Prediction Preserving Reducibility - L. Pitt and M. K. Warmuth |
| | | Automatic Programming of Behavior-Based Robots using Reinforcement Learning - S. Mahadevan and J. Connell |
