1992 | | | **The Utility of Knowledge in Inductive Learning** - *Michael Pazzani and Dennis Kibler* |

| | | **Approximate testing and learnability** - *K. Romanik* |

| | | **Inductive reasoning and Kolmogorov complexity** - *M. Li and P. Vitanyi* |

| | | **A stochastic approach to genetic information processing** - *A. Konagaya* |

| | | **Learning arithmetic read-once formulas** - *N. H. Bshouty, T. R. Hancock and L. Hellerstein* |

| | | **Learning Boolean read-once formulas with arbitrary symmetric and constant fan-in gates** - *N. H. Bshouty, T. R. Hancock and L. Hellerstein* |

| | | **An interactive knowledge transfer model and analysis of Mastermind game** - *K. Koyama and T. Lai* |

| | | **The learning complexity of smooth functions of a single variable** - *D. Kimber and P. Long* |

| | | **Monotonic language learning** - *S. Kapur* |

| | | **Learning stochastic functions by smooth simultaneous estimation** - *K. L. Buescher and P. R. Kumar* |

| | | **A Further Comparison of Splitting Rules for Decision-Tree Induction** - *Wray Buntine and Tim Niblett* |

| | | **Lower Bound Methods and Separation Results for On-Line Learning Models** - *Wolfgang Maass and György Turán* |

| | | **Incrementally Learning Time-Varying Half-planes** - *T. P. Anthony Kuh and R. L. Rivest* |

| | | **Cryptographic lower bounds on learnability of Boolean functions on the uniform distribution** - *M. Kharitonov* |

| | | **Notes on the PAC learning of geometric concepts with additional information** - *K. Kakihara and H. Imai* |

| | | **Characterizations of learnability for classes of { 0,...,n }-valued functions** - *S. Ben-David, N. Cesa-Bianchi and P. M. Long* |

| | | **Generalization versus classification** - *R. Wiehagen and C. H. Smith* |

| | | **Can finite samples detect singularities of real-valued functions?** - *S. Ben-David* |

| | | **Learning switching concepts** - *A. Blum and P. Chalasani* |

| | | **Learning in multi-agent environments** - *P. Brazdil* |

| | | **Interactive Concept-Learning and Constructive Induction by Analogy** - *Luc De Raedt and Maurice Bruynooghe* |

| | | **An improved boosting algorithm and its implications on learning complexity** - *Y. Freund* |

| | | **Towards a more comprehensive theory of learning in computers** - *P. M. Long* |

| | | **Learning recursive languages with bounded mind changes** - *S. Lange and T. Zeugmann* |

| | | **Recovery from multiple faults in relational theory** - *S. Tangkitvanich and M. Shimura* |

| | | **A note on the query complexity of learning DFA** - *J. L. Balcázar, J. Díaz, R. Gavaldà and O. Watanabe* |

| | | **A computational model of teaching** - *J. Jackson and A. Tomkins* |

| | | **Proving based on similarity** - *K. Fujita and M. Harao* |

| | | **A Reinforcement Connectionist Approach to Robot Path Finding in Non-Maze-Like Environments** - *José Del R. Millán and Carme Torras* |

| | | **Inductive inference of formal languages from positive data enumerated primitive-recursively** - *A. Sakurai* |

| | | **Polynomial-time identification of very regular languages in the limit** - *N. Tanida and T. Yokomori* |

| | | **Iterative weighted least squares algorithms for neural networks classifiers** - *T. Kurita* |

| | | **Four Types of Learning Curves** - *S. Amari, N. Fujita and S. Shinomoto* |

| | | **Editorial annoncing author is editor-in-chief** - *Thomas G. Dietterich* |

| | | **Which classes of elementary formal systems are polynomial-time learnable?** - *S. Miyano, A. Shinohara and T. Shinohara* |

| | | **Regularization learning of neural networks for generalization** - *S. Akaho* |

| | | **Polynomial time inference of unions of tree pattern languages** - *H. Arimura, T. Shinohara and S. Otsuki* |

| | | **A Reply to Honavar’s Book Review of Neural Network Design and the Complexity of Learning** - *J. Stephen Judd* |

| | | **Learning heirarchical rule sets** - *J. Kivinen, H. Mannila and E. Ukkonen* |

| | | **Towards learning by abstraction** - *S. Sakurai and M. Haraguchi* |

| | | **A logical analysis of relevance in analogy** - *J. Arima* |

| | | **Absolute error bounds for learning linear functions on line** - *E. J. Bernstein* |

| | | **The use of abstract primitives in representing the meaning of Verbs for understanding metaphors** - *M. Suwa and H. Motoda* |

| | | **BELLMAN STRIKES AGAIN! The growth rate of sample complexity with dimension for the nearest neighbor classifier** - *S. S. Venkatesh, R. R. Snapp and D. Psaltis* |

| | | **Polynomial time inference of a subclass of context-free transformations** - *H. Arimura, H. Ishizaka and T. Shinohara* |

| | | **Types of monotonic language learning and their characterization** - *S. Lange and T. Zeugmann* |

| | | **Efficient inductive inference of primitive prologs from positive data** - *H. Ishizaka, H. Arimura and T. Shinohara* |

| | | **A Unifying Approach to Monotonic Language Learning on Informant** - *S. Lange and T. Zeugmann* |

| | | **Knowledge-intensive learning in connectionist networks** - *A. Namatame* |

| | | **Some weak learning results** - *D. P. Helmbold and M. K. Warmuth* |

| | | **Preliminary study on program synthesis based on induction and verification** - *K. Furukawa* |

| | | **Transfer of Learning by Composing Solutions of Elemental Sequential Tasks** - *Satinder Pal Singh* |

| | | **Analogical reasoning as a form of hypothetical reasoning and justification-based knowledge acquisition** - *R. Orihara* |

| | | **Learning from Multiple Sources of Inaccurate Data** - *G. Baliga, S. Jain and A. Sharma* |

| | | **Learning Integer Lattices** - *D. Helmbold, R. Sloan and M. K. Warmuth* |

| | | **Universal forecasting algorithms** - *V. Vovk* |

| | | **Inductive inference with bounded mind changes** - *Y. Mukouchi* |

| | | **On the Power of Monotonic Language Learning** - *S. Lange and T. Zeugmann* |

| | | **Exact learning of read-k disjoint DNF and not-so-disjoint DNF** - *H. Aizenstein and L. Pitt* |

| | | **Inductive Inference From All Positive and Some Negative Data** - *T. Motoki* |

| | | **A Framework for Average Case Analysis of Conjunctive Learning Algorithms** - *Michael J. Pazzani and Wendy Sarrett* |

| | | **Discovery learning in intelligent tutoring systems** - *S. Otsuki* |

| | | **Polynomial uniform convergence and polynomial-sample learnability** - *A. Bertoni, P. Campadelli, A. Morpurgo and S. Panizza* |

| | | **Corrigendum to Types of noise in data for concept learning** - *R. H. Sloan* |

| | | **Improving Performance in Neural Networks Using a Boosting Algorithm** - *H. Drucker, R. Schapire and P. Simard* |

| | | **Learning non-parametric densities by finite-dimensional parametric hypotheses** - *K. Yamanishi* |

| | | **Bayesian Methods for Adaptive Models** - *D. MacKay* |

| | | **Universal sequential learning and decisions from individual data sequences** - *N. Merhav and M. Feder* |

| | | **Competitive learning by entropy minimization** - *R. Kamimura* |

| | | **Topics in Learning Theory** - *M. Kearns and U. Vazirani* |

| | | **Learning conjunctions of Horn clauses** - *D. Angluin, M. Frazier and L. Pitt* |

| | | **Explorations of an Incremental, Bayesian Algorithm for Categorization** - *John R. Anderson and Michael Matessa* |

| | | **The Convergence of TD $ for General $** - *Peter Dayan* |

| | | **PAC learning with generalized samples and an application to stochastic geometry** - *S. R. Kulkarni, S. K. Mitter, J. N. Tsitsiklis and O. Zeitouni* |

| | | **From inductive inference to algorithmic learning theory** - *R. Wiehagen* |

| | | **A noise model on learning sets of strings** - *Y. Sakakibara and R. Siromoney* |

| | | **Technical Note Q-Learning** - *Christopher J. C. H. Watkins and Peter Dayan* |

| | | **Case based learning in inductive inference** - *K. P. Jantke* |

| | | **A knowledge transfer model: from zero-knowledge toward full knowledge** - *Y. Tsukada and K. Koyama* |

| | | **On learning ring-sum expansions** - *P. Fischer and H. Simon* |

| | | **A form of analogy as an abductive inference** - *M. Haraguchi* |

| | | **Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization** - *Scott Dietzen and Frank Pfenning* |

| | | **On the Handling of Continuous-Valued Attributes in Decision Tree Generation** - *Usama M. Fayyad and Keki B. Irani* |

| | | **On the Problem of Local Minima in Backpropagation** - *Marco Gori and Alberto Tesi* |

| | | **The logic of molecular geneticists for the understanding of genetic information** - *Y. Sakaki* |

| | | **On the role of equivalence quries in MAT learning** - *S. Tani* |

| | | **Read-thrice DNF is hard to learn with membership and equivalence queries** - *H. Aizenstein, L. Hellerstein and L. Pitt* |

| | | **On the exact learning of formulas in parallel** - *N. H. Bshouty and R. Cleve* |

| | | **Computational Learning Theory** - *M. Anthony and N. Biggs* |

| | | **Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System** - *F. Bergadano et al.* |

| | | **Polynomial time learning with version spaces** - *Haym Hirsh* |

| | | **Machine Learning: A Maturing Field** - *Jaime Carbonell* |

| | | **On exact specification by examples** - *M. Anthony, G. Brightwell, D. Cohen and J. Shawe-Taylor* |

| | | **How Fast Can a Threshold Gate Learn?** - *W. Maass and G. Turán* |

| | | **Universal Prediction of Individual Sequences** - *M. Feder, N. Merhav and M. Gutman* |

| | | **PAB-decisions for Boolean and real-valued features** - *S. Anoulova, P. Fischer, S. Pölt and H. U. Simon* |

| | | **Fast Learning of k-term DNF Formulas with Queries** - *A. Blum and S. Rudich* |

| | | **On learning noisy theshold functions with finite precision weights** - *R. Meir and J. F. Fontanari* |

| | | **Robust trainability of single neurons** - *K. Höffgen and H. Simon* |

| | | **PAC-learnability of determinate logic programs** - *S. Duzeroski, S. Muggleton and S. Russell* |

| | | **On PAC learnability of functional dependencies** - *T. Akutsu and A. Takasu* |

| | | **Neural Network Design and the Complexity of Learning** - *Vasant Honavar* |

| | | **The Design and Analysis of Efficient Learning Algorithms** - *R. E. Schapire* |

| | | **Practical Issues in Temporal Difference Learning** - *Gerald Tesauro* |

| | | **Polynomial-time MAT learning of multilinear logic programs** - *K. Ito and A. Yamamoto* |

| | | **Self-Improving Reactive Agents Based On Reinforcement Learning, Planning and Teaching** - *Long-ji Lin* |

| | | **Analogical reasoning using elementary formal system with mismatch** - *T. Miyahara* |

| | | **A Universal Method of Scientific Inquiry** - *Daniel N. Osherson, Michael Stob and Scott Weinstein* |

| | | **Some improved sample complexity bounds in the probabilistic PAC learning model** - *J. Takeuchi* |

| | | **First Nearest Neighbor Classification on Frey and Slate’s Letter Recognition Problem** - *Terence C. Fogarty* |

| | | **Characterizations of learnability for classes of { 0,...,n }-valued functions** - *S. Ben-David, N. Cesa-Bianchi, D. Haussler and P. M. Long* |

| | | **On the Computational Complexity of Approximating Distributions by Probabilistic Automata** - *Naoki Abe and Manfred K. Warmuth* |

| | | **Reconstructing algebraic functions from mixed data** - *S. Ar, R. J. Lipton, R. Rubinfeld and M. Sudan* |

| | | **Abductive Explanation-Based Learning: A Solution to the Multiple Inconsistent Explanation Problem** - *William W. Cohen* |

| | | **Computational learning theory: survey and selected bibliography** - *D. Angluin* |

| | | **Proc. 5th Annu. Workshop on Comput. Learning Theory** - *B. Daley and D. Haussler* |

| | | **Learning with a slowly changing distribution** - *P. L. Bartlett* |

| | | **Strong Separation of Learning Classes** - *J. Case, K. J. Chen and S. Jain* |

| | | **A Learning Criterion for Stochastic Rules** - *Kenji Yamanishi* |

| | | **On learning systolic languages** - *T. Yokomori* |

| | | **Preservation of predictability under polynomially sparse variations and its applications** - *N. Abe* |

| | | **On the Necessity of Occam Algorithms** - *R. Board and L. Pitt* |

| | | **Learning DNF formulae under classes of probability distributions** - *M. Flammini, A. Marchetti-Spaccamela and L. K. Cera* |

| | | **Language learning from stochastic input** - *S. Kapur and G. Bilardi* |

| | | **Learning in parallel** - *J. Vitter and J. Lin* |

| | | **An application of Bernstein polynomials in PAC model** - *M. Matsuoka* |

| | | **Domains of attraction in autoassociative memory networks for character pattern recognition** - *K. Niijima* |

| | | **Query by committee** - *H. S. Seung, M. Opper and H. Sompolinsky* |

| | | **Characterization of pattern languages** - *Y. Mukouchi* |

| | | **ACE: a syntax-directed editor customizable from examples and queries** - *Y. Takada, Y. Sakakibara and T. Ohtani* |

| | | **Learning programs with an easy to calculate set of errors** - *W. I. Gasarch, R. K. Sitaraman, C. H. Smith and M. Velauthapillai* |

| | | **Degrees of inferability** - *P. Cholak, R. Downey, L. Fortnow, W. Gasarch, E. Kinber, M. Kummer, S. Kurtz and T. Slaman* |

| | | **On Learning Limiting Programs** - *J. Case, S. Jain and A. Sharma* |

| | | **Learning simpleBoolean concepts** - *N. Littlestone* |

| | | **A technique for upper bounding the spectral norm with applications to learning** - *M. Bellare* |

| | | **Learning Probabilistic Automata and Markov Chains via Queries** - *Wen-Guey Tzeng* |

| | | **Apple tasting and nearly one-sided learning** - *D. P. Helmbold, N. Littlestone and P. M. Long* |

| | | **Too Much Information Can be too Much for Efficient Learning** - *R. Wiehagen and T. Zeugmann* |

| | | **A Statistical Approach to Solving the EBL Utility Problem** - *Russell Greiner and Igor Jurišica* |

| | | **A training algorithm for optimal margin classifiers** - *B. E. Boser, I. M. Guyon and V. N. Vapnik* |

| | | **Protein secondary structure prediction based on stochastic-rule learning** - *H. Mamitsuka and K. Yamanishi* |

| | | **On the Study of First Language Acquisition** - *D. Osherson and S. Weinstein* |

| | | **Implementing Valiant’s Learnability Theory Using Random Sets** - *E. M. Oblow* |

| | | **Dominating distributions and learnability** - *G. M. Benedek and A. Itai* |

| | | **Analogy is NP-hard** - *S. Furuya and S. Miyano* |

| | | **On-line learning of rectangles** - *Z. Chen and W. Maass* |

| | | **Learning k-term monotone Boolean formulae** - *Y. Sakai and A. Maruoka* |

| | | **Random DFA’s can be approximately learned from sparse uniform examples** - *K. J. Lang* |

| | | **Entropy Optimization Principles with Applications** - *J. N. Kapur and H. K Kesavan* |

| | | **Implementation of heuristic problem solving process including analogical reasoning** - *K. Ueda and S. Nagano* |

| | | **Inferring finite automata with stochastic output functions and an application to map learning** - *T. Dean, D. Angluin, K. Basye, S. Engelson, L. Kaelbling, E. Kokkevis and O. Maron* |

| | | **Characterizations of Class Preserving Monotonic and Dual Monotonic Language Learning** - *T. Zeugmann, S. Lange and S. Kapur* |

| | | **Inductive inferability for formal languages from positive data** - *M. Sato and K. Umayahara* |

| | | **Learnability of description logics** - *W. W. Cohen and H. Hirsh* |

| | | **Planning with abstraction based on partial predicate mappings** - *Y. Okubo and M. Haraguchi* |

| | | **Relationships between PAC-learning algorithms and weak Occam algorithms** - *E. Takimoto and A. Maruoka* |

| | | **Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning** - *Ronald J. Williams* |

| | | **Learning effect of a dynamic thesaurus in associated information retrieval** - *H. Kimoto and T. Iwadera* |

| | | **Dynamic Parameter Encoding for Genetic Algorithms** - *Nicol N. Schraudolph and Richard K. Belew* |

| | | **Learning k-term DNF formulas with an incomplete membership oracle** - *S. A. Goldman and H. D. Mathias* |

| | | **On the sample complexity of PAC-learning using random and chosen examples** - *B. B. Eisenberg* |

| | | **A Comparison between Squared Error and Relative Entropy Metrics Using Several Optimization Algorithms** - *R. L. Watrous* |

| | | **A Bayesian Method for the Induction of Probabilistic Networks from Data** - *Gregory F. Cooper and Edward Herskovits* |

| January | | **Inferring Graphs from Walks** - *J. A. Aslam* |

| February | | **Algorithms for Exploring an Unknown Graph** - *M. Betke* |

| | | **Connectionist Modeling and Control of Finite-State Environments** - *J. R. Bachrach* |

| March | | **Learning via Queries to ** - *W. Gasarch, M. Pleszkoch and R. Solovay* |

| | | **Identifiability of Hidden Markov Information Sources and their Minimum Degrees of Freedom** - *H. Ito, S. Amari and K. Kobayashi* |

| April | | **Bayesian Training of Backpropagation Networks by the Hybrid Monte Carlo Method** - *R. M. Neal* |

| June | | **Some ideas on learning with directional feedback** - *I. Barland* |

| | | **Probabilistic Hill-Climbing: Theory and Applications** - *Russell Greiner* |

| July | | **Speeding inference by acquiring new concepts** - *Henry Kautz and Bart Selman* |

| | | **Polynomially Sparse Variations and Reducibility among prediction problems** - *N. Abe and O. Watanabe* |

| | | **Learning via Queries** - *W. Gasarch and C. H. Smith* |

| September | | **Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications** - *D. Haussler* |

| October | | **Learning Boolean Functions in an Infinite Attribute Space** - *A. Blum* |

| | | **Learning useful Horn approximations** - *Russell Greiner and Dale Schuurmans* |

| | | **Characterization of finite identification** - *Y. Mukouchi* |

| | | **A metric entropy bound is not sufficient for learnability** - *R. Dudley, S. Kulkarni, T. Richardson and O. Zeituni* |

| November | | **Neural Networks in Mathematica** - *J. A. Freeman* |

| December | | **H_-optimal estimation: A tutorial** - *U. Shaked and Y. Theodor* |