| 1994 | |  | Comparing methods for refining certainty-factor rule-bases - J. Jeffrey Mahoney and Raymond J. Mooney |
| | | An optimal parallel algorithm for learning DFA - J. L. Balcázar, J. Díaz, R. Gavaldà and O. Watanabe |
| | | Technical note: an upper bound on the loss from approximate optimal-value functions - Satinder P. Singh and Richard C. Yee |
| | | Hierarchical self-organization in genetic programming - Justinian P. Rosca and Dana H. Ballard |
| | | The Importance of Attribute Selection Measures in Decision Tree Induction - W. Z. Liu and A. P. White |
| | | Learning with malicious membership queries and exceptions - D. Angluin and M. Kriķis |
| | | On learning discretized geometric concepts - N. Bshouty |
| | | Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries - Thomas R. Hancock et al. |
| | | Learning Rules with Local Exceptions - J. Kivinen, H. Mannila and E. Ukkonen |
| | | An Upper Bound on the Loss from Approximate Optimal-Value Functions - Satinder P. Singh and Richard C. Yee |
| | | Refinements of Inductive Inference by Popperian and Reliable Machines - J. Case, S. Jain and S. Ngo Manguelle |
| | | Using Kullback-Leibler Divergence in Learning Theory - S. Anoulova and S. Pölt |
| | | Using Experts for Predicting Continuous Outcomes - J. Kivinen and M. Warmuth |
| | | Trading accuracy for simplicity in decision trees - Marko Bohanec and Ivan Bratko |
| | | In defense of C4.5: notes on learning one-level decision trees - Tapio Elomaa |
| | | Finding tree patterns consistent with positive and negative examples using queries - H. Ishizaka, H. Arimura and T. Shinohara |
| | | Induction inference of an approximate concept from positive data - Y. Mukouchi |
| | | Identifying nearly minimal Gödel numbers from additional information - R. Freivalds, O. Botuscharov and R. Wiehagen |
| | | Explicit Representation of Concept Negation - Jean-Francois Puget |
| | | Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments - Janusz Wnek and Ryszard S. Michalski |
| | | Monotonicity versus efficiency for learning languages from texts - E. Kinber |
| | | Three decades of team learning - C. H. Smith |
| | | The weighted majority algorithm - N. Littlestone and M. K. Warmuth |
| | | Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle - Dana Angluin and Donna K. Slonim |
| | | Derived sets and inductive inference - K. Apsītis |
| | | Using sampling and queries to extract rules from trained neural networks - Mark W. Craven and Jude W. Shavlik |
| | | Learning Probabilistic Read-once Formulas on Product Distributions - Robert E. Schapire |
| | | Learning disjunctive concepts by means of genetic algorithms - Attilio Giordana, Lorenza Saitta and Floriano Zini |
| | | Characterizing Language Learning by Standardizing Operations - S. Jain and A. Sharma |
| | | Predicting {0,1} Functions on Randomly Drawn Points - D. Haussler, N. Littlestone and M. K. Warmuth |
| | | Identifying regular languages over partially-commutative monoids - C. Ferretti and G. Mauri |
| | | Combining top-down and bottom-up techniques in inductive logic programming - John M. Zelle, Raymond J. Mooney and Joshua B. Konvisser |
| | | Learnability with restricted focus of attention guarantees noise-tolerance - S. Ben-David and E. Dichterman |
| | | Learning an Optimally Accurate Representation System - Russell Greiner and Dale Schuurmans |
| | | On the Complexity of Learning on Neural Nets - W. Maass |
| | | Inclusion problems in parallel learning and games - M. Kummer and F. Stephan |
| | | Quantifying Prior Determination Knowledge Using the PAC Learning Model - Sridhar Mahadevan and Prasad Tadepalli |
| | | Program Size Restrictions in Computational Learning - S. Jain and A. Sharma |
| | | Towards a better understanding of memory-based reasoning systems - John Rachlin, Simon Kasif, Steven Salzberg and David W. Aha |
| | | Learning linear threshold functions in the presence of classification noise - T. Bylander |
| | | Classification using information - W. I. Gasarch, M. G. Pleszkoch and M. Velauthapillai |
| | | A Neuroidal Model for Cognitive Functions - L. Valiant |
| | | The generate, test, and explain discovery system architecture - Michael de la Maza |
| | | Reward functions for accelerated learning - Maja J. Mataric |
| | | Average case analysis of pattern language learning algorithms - T. Zeugmann |
| | | Irrelevant features and the subset selection problem - George H. John, Ron Kohavi and Karl Pfleger |
| | | Recent Methods for RNA Modeling Using Stochastic Context-Free Grammars - Yasubumi Sakakibara, Michael Brown, Richard Hughey, I. Saira Mian, Kimmen Sjölander, Rebecca C. Underwood and David Haussler |
| | | Deductive plan generation - W. Bibel and M. Thielscher |
| | | Machine Learning and Qualitative Reasoning - Ivan Bratko |
| | | Guest Editor’s Introduction by Lisa Hellerstein - Lisa Hellerstein |
| | | Vacillatory learning of nearly minimal size grammers - J. Case, S. Jain and A. Sharma |
| | | Case-Based Learning: Predictive Features in Indexing - Colleen M. Seifert et al. |
| | | Structural results about on-line learning models with and without queries - P. Auer and P. M. Long |
| | | Small sample decision tree pruning - Sholom M. Weiss and Nitin Indurkhya |
| | | Learning Default Concepts - Dale Schuurmans and Russell Greiner |
| | | Therapy plan generation as program synthesis - O. Arnold and K. P. Jantke |
| | | Prototype and feature selection by sampling and random mutation hill climbing algorithms - David B. Skalak |
| | | Generalizing version spaces - Haym Hirsh |
| | | Training digraphs - H.-C. Tu and C. H. Smith |
| | | Learning probabilistic automata with variable memory length - D. Ron, Y. Singer and N. Tishby |
| | | Revision of production system rule-bases - Patrick M. Murphy and Michael J. Pazzani |
| | | On Case-Based Representability and Learnability of Languages - C. Globig and S. Lange |
| | | Higher-Order Neural Networks Applied to 2D and 3D Object Recognition - Lilly Spirkovska and Max B. Reid |
| | | Learning without state-estimation in partially observable Markovian decision processes - Satinder P. Singh, Tommi Jaakkola and Michael I. Jordan |
| | | Heterogeneous uncertainty sampling for supervised learning - David D. Lewis and Jason Catlett |
| | | A conservation law for generalization performance - Cullen Shaffer |
| | | Charcterization of Language Learning from Informant under various Monotonicity Constraints - S. Lange and T. Zeugmann |
| | | On-line learning from search failures - Neeraj Bhatnagar and Jack Mostow |
| | | From specifications to programs: induction in the service of synthesis - N. Dershowitz |
| | | An improved algorithm for incremental induction of decision trees - Paul E. Utgoff |
| | | Towards efficient inductive synthesis from input/output examples - J. Barzdinš |
| | | Improving accuracy of incorrect domain theories - Lars Asker |
| | | Stochastic Context-Free Grammars for tRNA modeling - Yasubumi Sakakibara, Michael Brown, Richard Hughey, I. Saira Mian, Kimmen Sjölander, Rebecca C. Underwood and David Haussler |
| | | The Power of Self-Directed Learning - S. A. Goldman and R. H. Sloan |
| | | Modeling Cognitive Development on Balance Scale Phenomena - Thomas R. Shultz, Denis Mareschal and William C. Schmidt |
| | | On the learnability of discrete distributions - M. Kearns, Y. Mansour, D. Ron, R. Rubinfeld, R. Schapire and L. Sellie |
| | | Algebraic Reasoning about Reactions: Discovery of Conserved Properties in Particle Physics - Raúl E. Valdés-Pérez |
| | | On Learning Simple Deterministic and Probabilistic Neural Concepts - M. Golea and M. Marchand |
| | | The Power of Prob in Popperian FINite learning - R. Daley, B. Kalyanasundaram and M. Velauthapillai |
| | | Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension - David Haussler, Michael Kearns and Robert E. Schapire |
| | | Machine learning and qualitative reasoning, extended abstract - Ivan Bratko |
| | | On learning counting fuctions with queries - Z. Chen and S. Homer |
| | | Efficient learning of regular expressions from good examples - A. Brāzma and Čerāns |
| | | A new method for predicting protein secondary structures based on stochastic tree grammars - Naoki Abe and Hiroshi Mamitsuka |
| | | Improved Sample Size Bounds for PAB-decisions - S. Pölt |
| | | Flattening and Saturation: Two Representation Changes for Generalization - Céline Rouveirol |
| | | Trial and Error: a New Approach to Space-bounded Learning - F. Ameur, P. Fischer, K. U. Höffgen and F. Meyer auf der Heide |
| | | Consideration of risk in reinforcement learning - Matthias Heger |
| | | Valid Generalisation of Functions from Close Approximations on a Sample - M. Anthony and J. Shawe-Taylor |
| | | Co-learning of total recursive functions - R. Freivalds, M. Karpinski and C. H. Smith |
| | | Markov games as a framework for multi-agent reinforcement learning - Michael L. Littman |
| | | Modeling cognitive development on balance-scale phenomena - Thomas R. Schultz, Denis Mareschal and William C. Schmidt |
| | | The strength of noninclusions for teams of finite learners - M. Kummer |
| | | Constructive induction for recursive programs - C. R. Mofizur and M. Numao |
| | | Inductive inference of monogenic pure context-free languages - N. Tanida and T. Yokomori |
| | | Learning Non-parametric Smooth Rules by Stochastic Rules with Finite Partitioning - K. Yamanishi |
| | | Approximate Inference and Scientific Method - M. A. Fulk and S. Jain |
| | | Getting the most from flawed theories - Moshe Koppel, Alberto Maria Segre and Ronen Feldman |
| | | Playing the matching-shoulders lob-pass game with logarithmic regret - J. Kilian, K. J. Lang and B. A. Pearlmutter |
| | | A Bayesian framework to integrate symbolic and neural learning - Irina Tchoumatchenko and Jean-Gabriel Ganascia |
| | | Efficient agnostic PAC-learning with simple hypotheses - W. Maass |
| | | On-line learning of rectangles and unions of rectangles - Zhixiang Chen and Wolfgang Maass |
| | | Learning one-dimensional geometric patterns under one-sided random misclassification noise - P. W. Goldberg and S. A. Goldman |
| | | Oracles and queries that are sufficient for exact learning - N. H. Bshouty, R. Cleve, S. Kannan and C. Tamon |
| | | Bounded degree graph inference from walks - Vijay Raghavan |
| | | A statistical approach to decision tree modeling - M. I. Jordan |
| | | Vacillatory learning of nearly-minimal size grammars - John Case, Sanjay Jain and Arun Sharma |
| | | Efficient distribution-free population learning of simple concepts - A. Nakamura, N. Abe and J. Takeuchi |
| | | Learning unions of boxes with membership and equivalence queries - P. W. Goldberg, S. A. Goldman and H. D. Mathais |
| | | Language learning under various types of constraint combinations - S. Kapur |
| | | Learning from examples with typed equational programming - A. Ishino and A. Yamamoto |
| | | Some New Directions in Computational Learning Theory - M. Frazier and L. Pitt |
| | | A unified approach to inductive logic and case-based reasoning - M. M. Richter |
| | | Guest Editor’s Introduction - Michael J. Pazzani |
| | | On learning read-k-satisfy-j DNF - A. Blum, R. Khardon, E. Kushilevitz, L. Pitt and D. Roth |
| | | Bias in Information-Based Measures in Decision Tree Induction - Allan P. White and Wei Zhong Liu |
| | | Tracking drifting concepts by minimizing disagreements - David P. Helmbold and Philip M. Long |
| | | Reducing misclassification costs - Michael Pazzani, Christopher Merz, Patrick Murphy, Kamal Ali, Timothy Hume and Clifford Brunk |
| | | Learning recursive relations with randomly selected small training sets - David W. Aha, Stephanie Lapointe, Charles X. Ling and Stan Matwin |
| | | Extremes in the Degrees of Inferability - L. Fortnow, W. Gasarch, S. Jain, E. Kinber, M. Kummer, S. Kurtz, M. Pleszkoch, T. Slaman, R. Solovay and F. Stephan |
| | | Learning Unions of Convex Polygons - P. Fischer |
| | | Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis - A. Blum, M. Furst, J. Jackson, M. Kearns, Y. Mansour and S. Rudich |
| | | Lower bounds on the VC-dimension of smoothly parametrized function classes - W. S. Lee, P. L. Bartlett and R. C. Williamson |
| | | Technical note: statistical methods for analyzing speedup learning experiments - Oren Etzioni and Ruth Etzioni |
| | | Boosting and other machine learning algorithms - Harris Drucker, Corinna Cortes, L. D. Jackel, Yann LeCun and Vladimir Vapnik |
| | | Learning concatenations of locally testable languages from positive data - S. Kobayashi and T. Yokomori |
| | | Statistical Methods for Analyzing Speedup Learning Experiments - Oren Etzioni and Ruth Etzioni |
| | | A Hidden Markov Model that finds genes in E. coli DNA - A. Krogh, I. S. Mian and D. Haussler |
| | | A Polynomial Approach to the Constructive Induction of Structural Knowledge - Jörg-Uwe Kietz and Katharina Morik |
| | | The Neural Network Loading Problem is Undecidable - H. Wiklicky |
| | | Neural Network-Based Vision for Precise Control of a Walking Robot - Dean A. Pomerleau |
| | | Recent advances in inductive logic programming - S. Muggleton |
| | | Toward an ideal trainer - Susan L. Epstein |
| | | Generalization in partially connected layered neural networks - K. H. Kwon, K. Kang and J. H. Oh |
| | | Using genetic search to refine knowledge-based neural networks - David W. Opitz and Jude W. Shavlik |
| | | Learning with queries but incomplete information - R. H. Sloan and G. Turán |
| | | Rule-generating abduction for recursive Prolog - K. Hirata |
| | | A modular Q-learning architecture for manipulator task decomposition - Chen K. Tham and Richard W. Prager |
| | | Introduction to the Abstracts of the Invited Talks Presented at ML92 Conference in Aberdeen, 1-3 July 1992 - D. Sleeman |
| | | Improving generalization with active learning - David Cohn, Les Atlas and Richard Ladner |
| | | Learning monotone log-term DNF formulas - Y. Sakai and A. Maruoka |
| | | Selective reformulation of examples in concept learning - Jean-Daniel Zucker and Jean-Gabriel Ganascia |
| | | Learning Local and Recognizable -languages and Monadic Logic Programs - A. Saoudi |
| | | On a learnability question associated to neural networks with continuous activations - B. DasGupta, H. T. Siegelmann and E. Sontag |
| | | Rule induction for semantic query optimization - Chun-Nan Hsu and Craig A. Knoblock |
| | | On learning arithmetic read-once formulas with exponentiation - D. Bshouty and N. H. Bshouty |
| | | The minimum description length principle and categorical theories - J. R. Quinlan |
| | | Simulating Access to hidden information while learning - P. Auer and P. Long |
| | | Algorithms and Lower Bounds for On-Line Learning of Geometrical Concepts - Wolfgang Maass and György Turán |
| | | A note on learning DNF formulas using equivalence and incomplete membership queries - Z. Chen |
| | | The learnability of description logics with equality constraints - William W. Cohen and Haym Hirsh |
| | | Classification of Predicates and Languages - R. Wiehagen, C. H. Smith and T. Zeugmann |
| | | Frequencies vs. biases: machine learning problems in natural language processing - abstract - F. C. N. Pereira |
| | | Machine Discovery in the Presence of Incomplete or Ambiguous Data - S. Lange and P. Watson |
| | | Ingnoring Data may be the only Way to Learn Efficiently - R. Wiehagen and T. Zeugmann |
| | | An efficient subsumption algorithm for inductive logic programming - Jörg-Uwe Kietz and Marcus Lübbe |
| | | Co-learnability and FIN-identifiability of enumerable classes of total recursive functions - R. Freivalds, D. Gobleja, M. Karpinski and C. H. Smith |
| | | Book Review: C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. - Steven L. Salzberg |
| | | Concept Formation During Interactive Theory Revision - Stefan Wrobel |
| | | Greedy attribute selection - Rich Caruana and Dayne Freitag |
| | | A Theory for Memory-Based Learning - Jyh-Han Lin and Jeffrey Scott Vitter |
| | | Exploiting random walks for learning - P. L. Bartlett, P. Fischer and K.-U. Höffgen |
| | | Learning from a consistently ignorant teacher - M. Frazier, S. Goldman, N. Mishra and L. Pitt |
| | | Matters Horn and Other Features in the Computational Learning Theory Landscape: The Notion of Membership - M. Frazier |
| | | Guest Editorial - Katharina Morik, Francesco Bergadano and Wray Buntine |
| | | Characterizing language identification by standardizing operations - Sanjay Jain and Arun Sharma |
| | | Open problems in Systems that learn - Mark Fulk, Sanjay Jain and Daniel N. Osherson |
| | | On learning discretized geometric concepts - Nader H. Bshouty, Zhixiang Chen and Steve Homer |
| | | Synthesis algorithm for recursive processes by -calculus - S. Kimura, A. Togashi and N. Shiratori |
| | | Mutual information gaining algorithm and its relation to PAC-learning algorithm - E. Takimoto, I. Tajika and A. Maruoka |
| | | Refutably probably approximately correct learning - S. Matsumoto and A. Shinohara |
| | | Efficient algorithms for minimizing cross validation error - Andrew W. Moore and Mary S. Lee |
| | | Rigorous learning curve bounds from statistical mechanics - D. Haussler, M. Kearns, H. S. Seung and N. Tishby |
| | | An algorithm to learn read-once threshold formulas, and transformations between learning models - N. Bshouty, T. Hancock, L. Hellerstein and M. Karpinski |
| | | Approximation and estimation bounds for artificial neural networks - Andrew R. Barron |
| | | Neural Networks: a Comprehensive Foundation - S. Haykin |
| | | To discount or not to discount in reinforcement learning: a case study comparing R learning and Q learning - Sridhar Mahadevan |
| | | Learning the CLASSIC Description Logic: Theoretical and Experimental Results - William W. Cohen and Haym Hirsh |
| | | Simulating the Child’s Acquisition of the Lexicon and Syntax - Experiences with Babel - Rick Kazman |
| | | Choosing a learning team: a topological approach - K. Apsitis, R. Freivalds and C. Smith |
| | | Acquisition of Children’s Addition Strategies: A Model of Impasse-Free, Knowledge-Level Learning - Randolph M. Jones and Kurt Vanlehn |
| | | Fuzzy analogy based reasoning and classification of fuzzy analogies - T. Iwatani, S. Tano, A. Inoue and W. Okamoto |
| | | On-line learning with malicious noise and the closure algorithm - P. Auer and N. Cesa-Bianchi |
| | | On Training Simple Neural Networks and Small-weight Neurons - T. Hegedüs |
| | | Toward efficient agnostic learning - Michael J. Kearns, Robert E. Schapire and Linda M. Sellie |
| | | Children, Adults, and Machines as Discovery Systems - David Klahr |
| | | A Calculus for logical clustering - S. Bai |
| | | Case-based learning: predictive features in indexing - Colleen M. Seifert, Kristian J. Hammond, Hollyn M. Johnson, Timothy M. Converse, Thomas F. McDougal and Scott W. VanderStoep |
| | | Associative Reinforcement Learning: A Generate and Test Algorithm - Leslie Pack Kaelbling |
| | | Average-Case Analysis of Pattern Language Learning Algorithms - T. Zeugmann |
| | | Learning languages by collecting classes and tuning parameters - Y. Sakakibara, K. P. Jantke and S. Lange |
| | | Bayesian inductive logic programming - S. Muggleton |
| | | Machine Learning of Higher Order Programs - G. Baliga, J. Case, S. Jain and M. Suraj |
| | | Asynchronous Stochastic Approximation and Q-Learning - John N. Tsitsiklis |
| | | Explanation-based reuse of Prolog programs - Y. Koga, E. Hirowatari and S. Arikawa |
| | | Discrete Sequence Prediction and Its Applications - Philip Laird and Ronald Saul |
| | | The minimum L-complexity algorithm and its applications to learning non-parametric rules - K. Yamanishi |
| | | Frequencies vs biases: machine learning problems in natural language processing - abstract - Fernando C. N. Pereira |
| | | TD converges with probability 1 - Peter Dayan and Terrence J. Sejnowski |
| | | Efficient inference of partial types - Dexter Kozen, Jens Palsberg and Michael I. Schwartzbach |
| | | Learning by experimentation: incremental refinement of incomplete planning domains - Yolanda Gil |
| | | Bounds on the Number of Examples needed for Learning Functions - H. U. Simon |
| | | On a Question in Learning Minimal Programs - S. Jain |
| | | Efficient learning of continuous neural networks - P. Koiran |
| | | Geometrical concept learning and convex polytopes - T. Hegedüs |
| | | Language learning from good examples - S. Lange, J. Nessel and R. Wiehagen |
| | | Inference and minimization of hidden Markov chains - D. Gillman and M. Sipser |
| | | Using neural networks to modularize software - Robert W. Schwanke and Stephen Joseé Hanson |
| | | Learning Languages by Collecting Cases and Tuning Parameters - K. P. Jantke Y. Sakakibara and S. Lange |
| | | Acquiring and Combining Overlapping Concepts - Joel D. Martin and Dorrit O. Billman |
| | | Approximate methods for sequential decision making using expert advice - T. H. Chung |
| | | Learning with higher order additional information - G. Baliga and J. Case |
| | | Incremental reduced error pruning - Johannes Fürnkranz and Gerhard Widmer |
| | | The representation of recursive languages and its impact on the efficiency of learning - S. Lange |
| | | Learning nonoverlapping perceptron networks from examples and membership queries - Thomas R. Hancock, Mostefa Golea and Mario Marchand |
| | | Fat-shattering and the learnability of real-valued functions - P. L. Bartlett, P. M. Long and R. C. Williamson |
| | | Enumerable classes of total recursive functions: complexity of inductive inference - A. Ambainis and J. Smotrovs |
| | | An efficient membership-query algorithm for learning DNF with respect to the uniform distribution - Jeffrey Jackson |
| | | A powerful heuristic for the discovery of complex patterned behavior - Raúl E. Valdés-Pérez and Aurora Pérez |
| | | Combining Symbolic and Neural Learning - Jude W. Shavlik |
| | | Towards realistic theories of learning - N. Abe |
| | | Combining symbolic and neural learning, extended abstract - Jude Shavlik |
| | | On the Power of Equivalence Queries - R. Gavaldà |
| | | An incremental learning approach for completable planning - Melinda T. Gervasio and Gerald F. DeJong |
| | | Learning with discrete multivalued neurons - Zoran Obradović and Ian Parberry |
| | | A constraint-based induction algorithm in FOL - Michèle Sebag |
| | | On the intrinsic complexity of language identification - S. Jain and A. Sharma |
| | | The effect of adding relevance information in a relevance feedback environment - C. Buckley, G. Salton and J. Allan |
| | | Incremental abductive EBL - William W. Cohen |
| | | Introduction Structured Connectionist Systems - Alex Waibel |
| | | Editorial: New Editorial Board Members - Thomas G. Dietterich |
| | | Efficient reinforcement learning - C. N. Fiechter |
| | | Complexity-based induction - Darrell Conklin and Ian H. Witten |
| | | PAC learning with irrelevant attributes - Aditi Dhagat and Lisa Hellerstein |
| | | Read-twice DNF Formulas are Properly Learnable - K. Pillaipakkamnatt and V. Raghavan |
| | | Associative Reinforcement Learning: Functions in k-DNF - Leslie Pack Kaelbling |
| | | The power of team exploration: two robots can learn unlabeled directed graphs - Michael A. Bender and Donna K. Slonim |
| | | Efficient algorithm for learning simple regular expressions from noisy examples - A. Brāzma |
| | | Information Geometry of the EM and em Algorithms for Neural Networks - S. Amari |
| | | Constructing predicate mappings for goal-directed abstraction - Y. Okubo and M. Haraguchi |
| | | An optimal-control application of two paradigms of on-line learning - V. G. Vovk |
| | | Learning structurally reversible context-free grammars from queries and counterexamples in polynomial time - A. Burago |
| | | Children, adults, and machines as discovery systems, extended abstract - David Klahr |
| | | On the limits of proper learnability of subclasses of DNF formulas - K. Pillaipakkamnatt and V. Raghavan |
| February | | Hidden Markov models in computational biology: Applications to protein modeling - A. Krogh, M. Brown, I. S. Mian, K. Sjölander and D. Haussler |
| March | | Exact learning of -DNF formulas with malicious membership queries - Dana Angluin |
| | | Optimal Sequential Probability Assignment for Individual Sequences - M. J. Weinberger, N. Merhav and M. Feder |
| | | On Using the Fourier transform to learn disjoint DNF - R. Khardon |
| May | | The bounded injury priority method and the learnability of unions of rectangles - Z. Chen and S. Homer |
| June | | Exponentiated Gradient Versus Gradient Descent for Linear Predictors - J. Kivinen and M. K. Warmuth |
| July | | Bounds on approximate steepest descent for likelihood maximization in exponential families - N. Cesa-Bianchi, A. Krogh and M. K. Warmuth |
| | | Exact Learning of Discretized Geometric Concepts - N. Bshouty, P. W. Goldberg, S. A. Goldman and H. D. Mathias |
| August | | Optimally Parsing a Sequence into Different Classes Based on Multiple Types of Information - G. D. Stormo and D. Haussler |
| | | RNA Modeling Using Gibbs Sampling and Stochastic Context Free Grammars - L. Grate, M. Herbster, R. Hughey, I. S. Mian, H. Noller and D. Haussler |
| October | | Program Synthesis in the Presence of Infinite Number of Inaccuracies - S. Jain |
| | | On monotonic strategies for learning r.e.\ languages - S. Jain and A. Sharma |
| December | | H^ Bounds for the recursive-least-squares algorithm - B. Hassibi and T. Kailath |
