| 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 |
