| 1991 | |  | Inductive Logic Programming - S. Muggleton |
| | | A ‘PAC’ Algorithm for Making Feature Maps - Philip Laird and Evan Gamble |
| | | On the learnability of infinitary regular sets - O. Maler and A. Pneuli |
| | | Simple translation-invariant concepts are hard to learn - M. Jerrum |
| | | Mistake bounds of incremental learners when concepts drift with applications to feedforward networks - T. Kuh, T. Petsche and R. Rivest |
| | | Neural net algorithms that learn in polynomial time from examples and queries - E. Baum |
| | | Improved learning of AC^0 functions - M. L. Furst, J. C. Jackson and S. W. Smith |
| | | A Reply to Reich’s Book Review of Exemplar-Based Knowledge Acquisition - Ray Bareiss |
| | | Learning curves in large neural networks - H. Sompolinsky, H. S. Seung and N. Tishby |
| | | Learning Time-Varying Concepts - A. Kuh, T. Petsche and R. L. Rivest |
| | | Learning to Perceive and Act by Trial and Error - Steven D. Whitehead and Dana H. Ballard |
| | | Rigel: An Inductive Learning System - Roberto Gemello, Franco Mana and Lorenza Saitta |
| | | A view of computational learning theory - Leslie Valiant |
| | | Editorial on field doing well causing backlog - Jaime G. Carbonell |
| | | Learnability with respect to Fixed Distributions - G. Benedek and A. Itai |
| | | Computer Systems that Learn - S. Weiss and C. Kulikowski |
| | | Elements of Information Theory - T. Cover and J. Thomas |
| | | Learning Automata from Ordered Examples - Sara Porat and Jerome A. Feldman |
| | | Universal Portfolios - T. M. Cover |
| | | Proc. 4th Annu. Workshop on Comput. Learning Theory - L. Valiant and M. Warmuth |
| | | Applications of Learning Theorems - V. Faber and J. Mycielski |
| | | Self-learning reaching motion of a multi-joint arm using a trial-and-error heuristic and a neural network - K. Amakawa |
| | | Symbolic and Neural Learning Algorithms: An Experimental Comparison - Jude W. Shavlik, Raymond J. Mooney and Geoffrey G. Towell |
| | | A Distance-Based Attribute Selection Measure for Decision Tree Induction - R. López De Mántaras |
| | | Learning by smoothing: a morphological approach - W. M. Kim |
| | | Improved Estimates for the Accuracy of Small Disjuncts - J. R. Quinlan |
| | | How to learn in an unknown environment - X. Deng, T. Kameda and C. Papadimitriou |
| | | Theory of Learning: What’s Hard and What’s Easy to Learn - R. L. Rivest |
| | | An Incremental Deductive Strategy for Controlling Constructive Induction in Learning from Examples - Renée Elio and Larry Watanabe |
| | | Teachability in computational learning - A. Shinohara and S. Miyano |
| | | One-Sided Error Probabilistic Inductive Inference and Reliable Frequency Identification - E. B. Kinber and T. Zeugmann |
| | | Evaluating the performance of a simple inductive procedure in the presence of overfitting error - A. Nobel |
| | | Reinforcement Learning Architectures for Animats - R. S. Sutton |
| | | Navigating in Unfamiliar Geometric Terrain - A. Blum, P. Raghavan and B. Schieber |
| | | Complexity results on learning by neural networks - J-H. Lin and J. S. Vitter |
| | | Proc. of the Second International Workshop on Algorithmic Learning Theory - S. Arikawa and A. Maruoka and T. Sato |
| | | Machine Learning: A Theoretical Approach - B. K. Natarajan |
| | | Measurability Constraints on PAC Learnability - S. Ben-David and G. M. Benedek |
| | | Problems of computational and information complexity in machine vision and learning - S. R. Kulkarni |
| | | Redundant noisy attributes, attribute errors, and linear threshold learning using Winnow - N. Littlestone |
| | | Learning monotone k -DNF formulas on product distributions - T. Hancock and Y. Mansour |
| | | A loss bound model for on-line stochastic prediction strategies - K. Yamanishi |
| | | Polynomial time Inference of Arbitrary Pattern Languages - S. Lange and R. Wiehagen |
| | | Information-Based Evaluation Criterion for Classifier’s Performance - Igor Kononenko and Ivan Bratko |
| | | On learning binary weights for majority functions - S. S. Venkatesh |
| | | The correct definition of finite elasticity: corrigendum to Identification of unions - T. Motoki, T. Shinohara and K. Wright |
| | | Learning to Predict Non-Deterministically Generated Strings - Moshe Koppel |
| | | A Loss-Bound Model for On-line Stochastic Prediction Strategies - K. Yamanishi |
| | | The Induction of Dynamical Recognizers - Jordan B. Pollack |
| | | Inductive Inference and Unsolvability - L. Adleman and M. Blum |
| | | SLUG: A Connectionist Architecture for Inferring the Structure of Finite-State Environments - Michael C. Mozer and Jonathan Bachrach |
| | | A Reply to Zito-Wolf’s Book Review of Learning Search Control Knowledge: An Explanation-Based Approach - Steven Minton |
| | | The Use of Background Knowledge in Decision Tree Induction - Marlon Nú\~nez |
| | | On the Role of Interpretive Analogy in Learning - B. Indurkhya |
| | | Inductive Inference of Monotonic Formal Systems From Positive Data - T. Shinohara |
| | | Fast identification of geometric objects with membership queries - W. J. Bultman and W. Maass |
| | | A Critical Look at Experimental Evaluations of EBL - Alberto Segre, Charles Elkan and Alexander Russell |
| | | Learning read-once formulas over fields and extended bases - T. Hancock and L. Hellerstein |
| | | Investigating the distribution assumptions in the PAC learning model - P. L. Bartlett and R. C. Williamson |
| | | Introduction - David S. Touretzky |
| | | Conflict Resolution as Discovery in Particle Physics - Sakir Kocabas |
| | | Computational complexity of learning read-once formulas over different bases - L. Hellerstein and M. Karpinski |
| | | Book Review - Roland J. Zito-Wolf |
| | | Tracking drifting concepts using random examples - D. P. Helmbold and P. M. Long |
| | | Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks - David Servan-Schreiber, Axel Cleeremans and James L. Mcclelland |
| | | Monotonic and Non-monotonic Inductive Inference - K. P. Jantke |
| | | Exemplar-Based Knowledge Acquisition - Yoram Reich |
| | | A Nearest Hyperrectangle Learning Method - Steven Salzberg |
| | | Can neural networks do better than the Vapnik-Chervonenkis bounds? - G. Tesauro and D. Cohn |
| | | Learning monotone DNF with an incomplete membership oracle - D. Angluin and D. K. Slonim |
| | | Searching in the presence of linearly bounded errors - J. A. Aslam and A. Dhagat |
| | | Learning Commutative Deterministic Finite State Automata in Polynomial Time - N. Abe |
| | | Polynomial learnability of probabilistic concepts with respect to the Kullback-Leibler divergence - N. Abe, J. Takeuchi and M. K. Warmuth |
| | | Learning Search Control Knowledge: An Explanation-Based Approach - Roland J. Zito-Wolf |
| | | Editorial on expanding from four to six issues per year - Jaime G. Carbonell |
| | | A geometric approach to threshold circuit complexity - V. Roychowdhury, K. Siu, A. Orlitsky and T. Kailath |
| | | Teachability in Computational Learning - T. Shinohara |
| | | Learning simple concepts under simple distributions - M. Li and P. M. B. Vitanyi |
| | | Machine Learning - R. L. Rivest and W. Remmele |
| | | Testing finite state machines - M. Yannakakis and D. Lee |
| | | When oracles do not help - T. A. Slaman and R. M. Solovay |
| | | Calculation of the learning curve of Bayes optimal classification algorithm for learning a perceptron with noise - M. Opper and D. Haussler |
| | | The VC-dimension vs. the statistical capacity for two layer networks with binary weights - C. Ji and D. Psaltis |
| | | Letter Recognition Using Holland-Style Adaptive Classifiers - Peter W. Frey and David J. Slate |
| | | Adaptive Filter Theory - S. Haykin |
| | | Learning in the Presence of Partial Explanations - S. Jain and A. Sharma |
| | | Simultaneous learning of concepts and simultaneous estimation of probabilities - K. Buescher and P. R. Kumar |
| | | Polynomial-time learning of very simple grammars from posistive data - T. Yokomori |
| | | The role of learning in autonomous robots - R. Brooks |
| | | Back Propagation Separates Where Perceptrons Do - E. D. Sontag and H. J. Sussmann |
| | | On the computational power of sigmoid versus Boolean threshold circuits - W. Maass, G. Schnitger and E. D. Sontag |
| | | Instance-Based Learning Algorithms - David W. Aha, Dennis Kibler and Marc K. Albert |
| | | Probably almost Bayes decisions - P. Fischer, S. Pölt and H. U. Simon |
| | | On-line learning with an oblivious environment and the power of randomization - W. Maass |
| | | Unsupervised learning of distributions on binary vectors using two layer networks - Y. Freund and D. Haussler |
| | | Relations between probabilistic and team one-shot learners - R. Daley, L. Pitt, M. Velauthapillia and T. Will |
| | | Distributed Representations, Simple Recurrent Networks, and Grammatical Structure - Jeffrey L. Elman |
| | | Determinate Literals in Inductive Logic Programming - J. R. Quinlan |
| | | Exact learning of read-twice DNF formulas - H. Aizenstein and L. Pitt |
| | | Learning 2$-DNF formulas and $k decision trees - T. R. Hancock |
| January | | Learning the fourier spectrum of probabilistic lists and trees - W. Aiello and M. Mihail |
| | | Results on Learnability and the Vapnik-Chervonenkis Dimension - N. Linial, Y. Mansour and R. L. Rivest |
| March | | On Learning from Queries and Counterexamples in the Presence of Noise - Y. Sakakibara |
| | | Restrictions on Grammar Size in Language Identification - S. Jain and A. Sharma |
| April | | Probably Approximately Optimal Derivation Strategies - Russell Greiner and Pekka Orponen |
| | | Probably Approximate Learning of Sets and Functions - B. K. Natarajan |
| June | | Testing As A Dual To Learning - K. Romanik |
| August | | Knowledge compilation using Horn approximations - Bart Selman and Henry Kautz |
| September | | N-Learners Problem: Fusion of Concepts - N. S. V. Rao, E. M. Oblow, C. W. Glover and G. E. Liepins |
| October | | Algorithmic Learning of Formal Languages and Decision Trees - Y. Sakakibara |
| December | | Equivalence of Models for Polynomial Learnability - D. Haussler, M. Kearns, N. Littlestone and M. K. Warmuth |
| | | Inferring a Tree from Walks - O. Maruyama and S. Miyano |
