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* |