1994 | | | **On Learning Simple Deterministic and Probabilistic Neural Concepts** - *M. Golea and M. Marchand* |

| | | **Using Experts for Predicting Continuous Outcomes** - *J. Kivinen and M. Warmuth* |

| | | **Valid Generalisation of Functions from Close Approximations on a Sample** - *M. Anthony and J. Shawe-Taylor* |

| | | **The strength of noninclusions for teams of finite learners** - *M. Kummer* |

| | | **Associative methods in reinforcement learning: an empirical study** - *Leslie Pack Kaelbling* |

| | | **The Power of Self-Directed Learning** - *S. A. Goldman and R. H. Sloan* |

| | | **On the Complexity of Learning on Neural Nets** - *W. Maass* |

| | | **An efficient subsumption algorithm for inductive logic programming** - *Jörg-Uwe Kietz and Marcus Lübbe* |

| | | **Hard questions about easy tasks: issues from learning to play games** - *Susan L. Epstein* |

| | | **Approximate methods for sequential decision making using expert advice** - *T. H. Chung* |

| | | **Predicting {0,1} Functions on Randomly Drawn Points** - *D. Haussler, N. Littlestone and M. K. Warmuth* |

| | | **Rule induction for semantic query optimization** - *Chun-Nan Hsu and Craig A. Knoblock* |

| | | **Classification of Predicates and Languages** - *R. Wiehagen, C. H. Smith and T. Zeugmann* |

| | | **Simple Translation-Invariant Concepts Are Hard to Learn** - *M. Jerrum* |

| | | **Open problems in Systems that learn** - *Mark Fulk, Sanjay Jain and Daniel N. Osherson* |

| | | **Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension** - *David Haussler, Michael Kearns and Robert E. Schapire* |

| | | **Generalizing version spaces** - *Haym Hirsh* |

| | | **Choosing a learning team: a topological approach** - *K. Aps\=ıtis, R. Freivalds and C. Smith* |

| | | **On-line learning of rectangles and unions of rectangles** - *Zhixiang Chen and Wolfgang Maass* |

| | | **Efficient agnostic PAC-learning with simple hypotheses** - *W. Maass* |

| | | **Enumerable Classes of Total Recursive Functions: Complexity of Inductive Inference** - *Andris Ambainis and Juris Smotrovs* |

| | | **Quantifying Prior Determination Knowledge Using the PAC Learning Model** - *Sridhar Mahadevan and Prasad Tadepalli* |

| | | **Read-twice DNF Formulas are Properly Learnable** - *K. Pillaipakkamnatt and V. Raghavan* |

| | | **Complexity-based induction** - *Darrell Conklin and Ian H. Witten* |

| | | **Boosting and other machine learning algorithms** - *Harris Drucker, Corinna Cortes, L. D. Jackel, Yann LeCun and Vladimir Vapnik* |

| | | **Neural network modeling of physiological processes** - *Volker Tresp, John Moody and Wolf-Rüdiger Delong* |

| | | **Cryptographic limitations on learning Boolean formulae and finite automata** - *Michael Kearns and Leslie Valiant* |

| | | **Identifying Regular Languages over Partially-Commutative Monoids** - *Claudio Ferretti and Giancarlo Mauri* |

| | | **Modeling Cognitive Development on Balance Scale Phenomena** - *Thomas R. Schultz, Denis Mareschal and William C. Schmidt* |

| | | **A Theory for Memory-Based Learning** - *Jyh-Han Lin and Jeffrey Scott Vitter* |

| | | **Guest Editor's Introduction** - *Lisa Hellerstein* |

| | | **Learning non-deterministic finite automata from queries and counterexamples** - *Takashi Yokimori* |

| | | **Getting the most from flawed theories** - *Moshe Koppel, Alberto Maria Segre and Ronen Feldman* |

| | | **Neural Network-Based Vision for Precise Control of a Walking Robot** - *Dean A. Pomerleau* |

| | | **Learning probabilistic automata with variable memory length** - *D. Ron, Y. Singer and N. Tishby* |

| | | **Machine learning and qualitative reasoning** - *Ivan Bratko* |

| | | **Reward functions for accelerated learning** - *Maja J. Mataric* |

| | | **Learning Probabilistic Read-once Formulas on Product Distributions** - *Robert E. Schapire* |

| | | **Reducing misclassification costs** - *Michael Pazzani, Christopher Merz, Patrick Murphy, Kamal Ali, Timothy Hume and Clifford Brunk* |

| | | **Toward efficient agnostic learning** - *Michael J. Kearns, Robert E. Schapire and Linda M. Sellie* |

| | | **From Specifications to Programs: Induction in the Service of Synthesis** - *Nachum Dershowitz* |

| | | **Learning with queries but incomplete information** - *R. H. Sloan and G. Turán* |

| | | **Learning monotone log-term DNF formulas** - *Y. Sakai and A. Maruoka* |

| | | **Statistical Methods for Analyzing Speedup Learning Experiments** - *Oren Etzioni and Ruth Etzioni* |

| | | **Machine Discovery in the Presence of Incomplete or Ambiguous Data** - *S. Lange and P. Watson* |

| | | **Explanation-Based Reuse of Prolog Programs** - *Yasuyuki Koga, Eiju Hirowatari and Setsuo Arikawa* |

| | | **How fast can a threshold gate learn?** - *Wolgang Maass and György Turán* |

| | | **A statistical approach to decision tree modeling** - *M. I. Jordan* |

| | | **Evolution of a subsumption architecture that performs a wall following task for an autonomous mobile robot** - *John R. Koza* |

| | | **Contrastive learning with graded random networks** - *Javier R. Movellan and James L. McClelland* |

| | | **Efficient distribution-free learning of probabilistic concepts** - *Michael J. Kearns and Robert E. Schapire* |

| | | **Sensitivity constraints in learning** - *Scott H. Clearwater and Yongwon Lee* |

| | | **Inclusion problems in parallel learning and games** - *M. Kummer and F. Stephan* |

| | | **On Learning Monotone DNF Formulae under Uniform Distributions** - *L. Kucera, A. Marchettispaccamela and M. Protasi* |

| | | **A Neuroidal Model for Cognitive Functions** - *L. Valiant* |

| | | **The power of team exploration: two robots can learn unlabeled directed graphs** - *Michael A. Bender and Donna K. Slonim* |

| | | **Inducing probabilistic grammars by Byasian model merging** - *A. Stolcke and S. Omohundro* |

| | | **Small sample decision tree pruning** - *Sholom M. Weiss and Nitin Indurkhya* |

| | | **A Calculus for Logical Clustering** - *Shuo Bai* |

| | | **Filter likelihoods and exhaustive learning** - *David H. Wolpert* |

| | | **Using Kullback-Leibler Divergence in Learning Theory** - *S. Anoulova and S. Pölt* |

| | | **A Unified Approach to Inductive Logic and Case-Based Reasoning** - *Michael M. Richter* |

| | | **The effect of adding relevance information in a relevance feedback environment** - *C. Buckley, G. Salton and J. Allan* |

| | | **Improving accuracy of incorrect domain theories** - *L. Asker* |

| | | **Incremental reduced error pruning** - *Johannes Fürnkranz and Gerhard Widmer* |

| | | **Defining the limits of analogical planning** - *Diane J. Cook* |

| | | **Learning stochastic regular grammars by means of a state merging method** - *R. Carrasco and J. Oncina* |

| | | **Predicate invention and utilization** - *S. Muggleton* |

| | | **Therapy Plan Generation as Program Synthesis** - *Oksana Arnold and Klaus P. Jantke* |

| | | **Constructive Induction for Recursive Programs** - *Chowdhury Rahman Mofizur and Masayuki Numao* |

| | | **Language learning under various types of constraint combinations** - *Shyam Kapur* |

| | | **Learning by experimentation: incremental refinement of incomplete planning domains** - *Yolanda Gil* |

| | | **Fat-shattering and the learnability of real-valued functions** - *P. L. Bartlett, P. M. Long and R. C. Williamson* |

| | | **A comparitive study of the Kohonen self-organizing map and the elastic net** - *Yiu-fai Wong* |

| | | **On the intrinsic complexity of language identification** - *S. Jain and A. Sharma* |

| | | **Hierarchical self-organization in genetic programming** - *Justinian P. Rosca and Dana H. Ballard* |

| | | **Induction Inference of an Approximate Concept from Positive Data** - *Yasuhito Mukouchi* |

| | | **Minimal Samples of Positive Examples Identifying k-CNF Boolean Functions** - *A. T. Ogielski* |

| | | **Hamiltonian dynamics of neural networks** - *Ulrich Ramacher* |

| | | **Heterogeneous uncertainty sampling for supervised learning** - *David D. Lewis and Jason Catlett* |

| | | **Experiments on the transfer of knowledge between neural networks** - *Lorien Y. Pratt* |

| | | **Learning Languages by Collecting Cases and Tuning Parameters** - *Yasubumi Sakakibara, Klaus P. Jantke and Steffen Lange* |

| | | **A new method for predicting protein secondary structures based on stochastic tree grammars** - *Naoki Abe and Hiroshi Mamitsuka* |

| | | **Algorithms and Lower Bounds for On-Line Learning of Geometrical Concepts** - *Wolfgang Maass and György Turán* |

| | | **Simulating Access to hidden information while learning** - *P. Auer and P. Long* |

| | | **Learning disjunctive concepts using domain knowledge** - *Harish Ragavan and Larry Rendell* |

| | | **Average case analysis of k-CNF and k-DNF learning algorithms** - *Daniel S. Hirschberg, Michael J. Pazzani and Kamal M. Ali* |

| | | **Co-learning of total recursive functions** - *R. Freivalds, M. Karpinski and C. H. Smith* |

| | | **Efficient Learning of Regular Expressions from Good Examples** - *Alvis Brāzma and Kārlis Čerāns* |

| | | **An optimal-control application of two paradigms of on-line learning** - *V. G. Vovk* |

| | | **Data-driven inductive inference of finite-state automata** - *J. Gregor* |

| | | **A modular Q-learning architecture for manipulator task decomposition** - *Chen K. Tham and Richard W. Prager* |

| | | **A constraint-based induction algorithm in FOL** - *Michèle Sebag* |

| | | **An algorithm to learn read-once threshold formulas, and transformations between learning models** - *N. Bshouty, T. Hancock, L. Hellerstein and M. Karpinski* |

| | | **The Importance of Attribute Selection Measures in Decision Tree Induction** - *W. Z. Liu and A. P. White* |

| | | **Learning one-dimensional geometric patterns under one-sided random misclassification noise** - *P. W. Goldberg and S. A. Goldman* |

| | | **Tracking drifting concepts by minimizing disagreements** - *David P. Helmbold and Philip M. Long* |

| | | **Learning nonoverlapping perceptron networks from examples and membership queries** - *Thomas R. Hancock, Mostefa Golea and Mario Marchand* |

| | | **Learning Default Concepts** - *Dale Schuurmans and Russell Greiner* |

| | | **Bayesian inductive logic programming** - *S. Muggleton* |

| | | **Using genetic search to refine knowledge-based neural networks** - *David W. Opitz and Jude W. Shavlik* |

| | | **Training Digraphs** - *Hsieh-Chang Tu and Carl H. Smith* |

| | | **In defense of C4.5: notes on learning one-level decision trees** - *Tapio Elomaa* |

| | | **Weight elimination and effective network size** - *Andreas S. Weigend and David E. Rumelhart* |

| | | **Learning with malicious membership queries and exceptions** - *D. Angluin and M. Kriķis* |

| | | **Some New Directions in Computational Learning Theory** - *M. Frazier and L. Pitt* |

| | | **The Neural Network Loading Problem is Undecidable** - *H. Wiklicky* |

| | | **Program Size Restrictions in Computational Learning** - *Sanjay Jain and Arun Sharma* |

| | | **Using neural networks to modularize software** - *Robert W. Schwanke and Joseé Stephen Hanson* |

| | | **Recent advances in inductive logic programming** - *S. Muggleton* |

| | | **Learning Boolean formulas** - *Michael Kearns, Ming Li and Leslie Valiant* |

| | | **Geometrical concept learning and convex polytopes** - *T. Hegedüs* |

| | | **Introduction to the Abstracts of the Invited Talks Presented at ML92 Conference in Aberdeen, 1-3 July 1992** - *D. Sleeman* |

| | | **An optimal parallel algorithm for learning DFA** - *J. L. Balcázar, J. Díaz, R. Gavaldà and O. Watanabe* |

| | | **The Learnability of Description Logics with Equality Constraints** - *William W. Cohen and Haym Hirsh* |

| | | **Evaluation of learning biases using probabilistic domain knowledge** - *Marie desJardins* |

| | | **Using sampling and queries to extract rules from trained neural networks** - *Mark W. Craven and Jude W. Shavlik* |

| | | **Algebraic Reasoning about Reactions: Discovery of Conserved Properties in Particle Physics** - *Raúl E. Valdés-Pérez* |

| | | **Case-Based Learning: Predictive Features in Indexing** - *C. M. Seifert, K. J. Hammond, H. M. Johnson, T. M. Converse, T. F. Mcdoughal and S. W. Vanderstoep* |

| | | **How loading complexity is affected by node function sets** - *Stephen Judd* |

| | | **Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments** - *Janusz Wnek and Ryszard S. Michalski* |

| | | **Learning Rules with Local Exceptions** - *J. Kivinen, H. Mannila and E. Ukkonen* |

| | | **Efficient learning of continuous neural networks** - *P. Koiran* |

| | | **Infinitary Self-Reference in Learning Theory** - *J. Case* |

| | | **Learning the CLASSIC Description Logic: Theoretical and Experimental Results** - *William W. Cohen and Haym Hirsh* |

| | | **Inference and minimization of hidden Markov chains** - *D. Gillman and M. Sipser* |

| | | **Machine Learning of Higher Order Programs** - *G. Baliga, J. Case, S. Jain and M. Suraj* |

| | | **On a learnability question associated to neural networks with continuous activations** - *B. DasGupta, H. T. Siegelmann and E. Sontag* |

| | | **Toward an ideal trainer** - *Susan L. Epstein* |

| | | **On Case-Based Representability and Learnability of Languages** - *Christoph Globig and Steffen Lange* |

| | | **Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis** - *A. Blum, M. Furst, J. Jackson, M. Kearns, Y. Mansour and S. Rudich* |

| | | **On learning read-k-satisfy-j DNF** - *A. Blum, R. Khardon, E. Kushilevitz, L. Pitt and D. Roth* |

| | | **Vacillatory learning of nearly-minimal size grammars** - *John Case, Sanjay Jain and Arun Sharma* |

| | | **An inductive inference appraoch to classification** - *R. Freivalds and A. Hoffmann* |

| | | **An incremental concept formation approach for learning from databases** - *Robert Godin and Rokia Missaoui* |

| | | **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* |

| | | **Irrelevant features and the subset selection problem** - *George H. John, Ron Kohavi and Karl Pfleger* |

| | | **A Hidden Markov Model that finds genes in E. coli DNA** - *A. Krogh, I. S. Mian and D. Haussler* |

| | | **A Note on Learning DNF Formulas Using Equivalence and Incomplete Membership Queries** - *Zhixiang Chen* |

| | | **The minimum L-complexity algorithm and its applications to learning non-parametric rules** - *K. Yamanishi* |

| | | **Comparing methods for refining certainty-factor rule-bases** - *J. Jeffrey Mahoney and Raymond J. Mooney* |

| | | **Simulation results for a new two-armed bandit heuristic** - *Ronald L. Rivest and Yiqun Yin* |

| | | **Using knowledge-based neural networks to refine roughly-correct information** - *Geoffrey G. Towell and Jude W. Shavlik* |

| | | **Learnability with Restricted Focus of Attention Guarantees Noise-Tolerance** - *Shai Ben-David and Eli Dichterman* |

| | | **Learning with Higher Order Additional Information** - *Ganesh Baliga and John Case* |

| | | **Three Decades of Team Learning** - *Carl H. Smith* |

| | | **Synthesis Algorithm for Recursive Processes by ***mu*-calculus - *Shigemoto Kimura, Atsushi Togashi and Norio Shiratori* |

| | | **Learning theoretical terms** - *Ranan B. Banerji* |

| | | **An improved algorithm for incremental induction of decision trees** - *Paul E. Utgoff* |

| | | **Oracles and queries that are sufficient for exact learning** - *N. H. Bshouty, R. Cleve, S. Kannan and C. Tamon* |

| | | **Bayes decisions in a neural network-PAC setting** - *Svetlana Anulova, Jorge R. Cuellar, Klaus-U. Höffgen and Hans-U. Simon* |

| | | **A Polynomial Approach to the Constructive Induction of Structural Knowledge** - *Jörg-Uwe Kietz and Katharina Morik* |

| | | **Trial and Error: a New Approach to Space-bounded Learning** - *F. Ameur, P. Fischer, K. U. Höffgen and F. Meyer auf der Heide* |

| | | **Unsupervised learning for mobile robot navigation using probabilistic data association** - *Ingemar J. Cox and John J. Leonard* |

| | | **Learning in abstraction space** - *George Drastal* |

| | | **Bias in Information-Based Measures in Decision Tree Induction** - *Allan P. White and Wei Zhong Liu* |

| | | **Trading accuracy for simplicity in decision trees** - *Marko Bohanec and Ivan Bratko* |

| | | **Learning with discrete multivalued neurons** - *Zoran Obradović and Ian Parberry* |

| | | **Learning unions of boxes with membership and equivalence queries** - *P. W. Goldberg, S. A. Goldman and H. D. Mathais* |

| | | **When are k-nearest neighbor and backpropagation accurate for feasible-sized sets of examples?** - *Eric. B. Baum* |

| | | **Knowledge Acquisition from Amino Acid Sequences by Machine Learning System BONSAI** - *S. Shimozono, A. Shinohara, T. Shinohara, S. Miyano, S. Kuhara and S. Arikawa* |

| | | **TD(***lambda*) converges with probability 1 - *Peter Dayan and Terrence J. Sejnowski* |

| | | **Lower bounds on the VC-dimension of smoothly parametrized function classes** - *W. S. Lee, P. L. Bartlett and R. C. Williamson* |

| | | **Frequencies vs biases: machine learning problems in natural language processing - abstract** - *Fernando C. N. Pereira* |

| | | **Flattening and Saturation: Two Representation Changes for Generalization** - *Céline Rouveirol* |

| | | **Combining symbolic and neural learning, extended abstract** - *Jude Shavlik* |

| | | **Learning from a consistently ignorant teacher** - *M. Frazier, S. Goldman, N. Mishra and L. Pitt* |

| | | **Bounded degree graph inference from walks** - *Vijay Raghavan* |

| | | **Approximation and estimation bounds for artificial neural networks** - *Andrew R. Barron* |

| | | **On the limits of proper learnability of subclasses of DNF formulas** - *K. Pillaipakkamnatt and V. Raghavan* |

| | | **Explicit Representation of Concept Negation** - *Jean-Francois Puget* |

| | | **Guest Editor's Introduction** - *Michael J. Pazzani* |

| | | **Efficient reinforcement learning** - *C. N. Fiechter* |

| | | **Neural Networks: a Comprehensive Foundation** - *S. Haykin* |

| | | **Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory** - *Manfred Warmuth* |

| | | **Prototype and feature selection by sampling and random mutation hill climbing algorithms** - *David B. Skalak* |

| | | **On learning discretized geometric concepts** - *N. Bshouty* |

| | | **Probabilistic hill-climbing** - *William W. Cohen, Russell Greiner and Dale Schuurmans* |

| | | **Efficient algorithms for minimizing cross validation error** - *Andrew W. Moore and Mary S. Lee* |

| | | **Efficient NC algorithms for set cover with applications to learning and geometry** - *Bonnie Berger, John Rompel and Peter W. Shor* |

| | | **Rich Classes Inferable from Positive Data: Length-Bounded Elementary Formal Systems** - *Takeshi Shinohara* |

| | | **Learning hard concepts through constructive induction: framework and rationale** - *Larry Rendell and Raj Seshu* |

| | | **The power of probabilism in popperian FINite learning** - *R. Daley, B. Kalyanasundaram and M. Velauthapillai* |

| | | **Stochastic Context-Free Grammars for tRNA modeling** - *Yasubumi Sakakibara, Michael Brown, Richard Hughey, I. Saira Mian, Kimmen Sjölander, Rebecca C. Underwood and David Haussler* |

| | | **Markov games as a framework for multi-agent reinforcement learning** - *Michael L. Littman* |

| | | **Learning fixed point patterns by recurrent networks** - *Leong Kwan Li* |

| | | **The generate, test, and explain discovery system architecture** - *Michael de la Maza* |

| | | **Playing the matching-shoulders lob-pass game with logarithmic regret** - *J. Kilian, K. J. Lang and B. A. Pearlmutter* |

| | | **On the perceptron learning algorithm on data with high precision** - *Kai-Yeung Siu, Amir Dembo and Thomas Kailath* |

| | | **Fuzzy Analogy Based Reasoning and Classification of Fuzzy Analogies** - *Toshiharu Iwatani, Shun'ichi Tano, Atsushi Inoue and Wataru Okamoto* |

| | | **Weakening the language bias in LINUS** - *N. Lavrac and S. Džeroski* |

| | | **Generalization in partially connected layered neural networks** - *K. H. Kwon, K. Kang and J. H. Oh* |

| | | **Inductive Inference of Prolog Programs with linear dependency from positive data** - *H. Arimura and T. Shinohara* |

| | | **Projection pursuit learning: some theoretical issues** - *Ying Zhao and Christopher G. Atkeson* |

| | | **Improved Sample Size Bounds for PAB-decisions** - *S. Pölt* |

| | | **Detecting structure in small datasets by network fitting under complexity constraints** - *W. Finnoff and H. G. Zimmermann* |

| | | **Refinements of Inductive Inference by Popperian and Reliable Machines** - *John Case, Sanjay Jain and Suzanne Ngo-Manguelle* |

| | | **Inference of context-free grammars by enumeration: Structural containment as an ordering bias** - *J. Y. Giordano* |

| | | **Introduction Structured Connectionist Systems** - *Alex Waibel* |

| | | **Finding Minimal Generalizations for Unions of Pattern Languages and Its Application to Inductive Inference from Positive Data** - *H. Arimura, T. Shinohara and S. Otsuki* |

| | | **The weighted majority algorithm** - *N. Littlestone and M. K. Warmuth* |

| | | **Learning Concatenations of Locally Testable Languages from Positive Data** - *Satoshi Kobayashi and Takashi Yokomori* |

| | | **Approximate Inference and Scientific Method** - *M. A. Fulk and S. Jain* |

| | | **Book Review: C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993.** - *Steven L. Salzberg* |

| | | **Towards efficient inductive synthesis from input/output examples** - *Jānis Barzdinš* |

| | | **Children, adults, and machines as discovery systems** - *David Klahr* |

| | | **Composite Geometric Concepts and Polynomial Predictability** - *P. M. Long and M. K. Warmuth* |

| | | **Learning disjunctive concepts by means of genetic algorithms** - *Attilio Giordana, Lorenza Saitta and Floriano Zini* |

| | | **Regular grammatical inferencefrom positive and negative samples by genetic search: The GIG method** - *P. Dupon* |

| | | **Associative Reinforcement Learning: A Generate and Test Algorithm** - *Leslie Pack Kaelbling* |

| | | **Ignoring data may be the only way to learn efficiently** - *R. Wiehagen and T. Zeugmann* |

| | | **Towards a better understanding of memory-based reasoning systems** - *John Rachlin, Simon Kasif, Steven Salzberg and David W. Aha* |

| | | **Rule-Generating Abduction for Recursive Prolog** - *Kouichi Hirata* |

| | | **A schema for using multiple knowledge** - *Matjaž Gams, Marko Bohanec and Bojan Cestnik* |

| | | **Learning properties of multi-layer perceptrons with and without feedback** - *D. Gawronska, B. Schürmann and J. Hollatz* |

| | | **On learning arithmetic read-once formulas with exponentiation** - *D. Bshouty and N. H. Bshouty* |

| | | **PAC learning with irrelevant attributes** - *Aditi Dhagat and Lisa Hellerstein* |

| | | **A Formal Model of Hierarchical Concept-Learning** - *R. L. Rivest and R. Sloan* |

| | | **Rigorous learning curve bounds from statistical mechanics** - *D. Haussler, M. Kearns, H. S. Seung and N. Tishby* |

| | | **Discrete Sequence Prediction and Its Applications** - *Philip Laird and Ronald Saul* |

| | | **Experience with a Learning Personal Assistant** - *Tom M. Mitchell, Rich Caruana, Dayne Freitag, John P. McDermott and David Zabowski* |

| | | **Binary decision trees and an 'average-case' model for concept learning: implications for feature construction and the study of bias** - *Raj Seshu* |

| | | **Learning Non-parametric Smooth Rules by Stochastic Rules with Finite Partitioning** - *K. Yamanishi* |

| | | **A conservation law for generalization performance** - *Cullen Shaffer* |

| | | **Learning from data with bounded inconsistency: theoretical and experimental results** - *Haym Hirsh and William W. Cohen* |

| | | **The inference of tree languages from finite samples: an algebraic approach** - *Timo Knuutila and Magnus Steinby* |

| | | **Asynchronous Stochastic Approximation and Q-Learning** - *John N. Tsitsiklis* |

| | | **Acquiring and Combining Overlapping Concepts** - *Joel D. Martin and Dorrit O. Billman* |

| | | **A connectionist model of the learning of personal pronouns in English** - *Thomas R. Shultz, David Buckingham and Yuriko Oshima-Takane* |

| | | **Combining Symbolic and Neural Learning** - *Jude W. Shavlik* |

| | | **On learning discretized geometric concepts** - *Nader H. Bshouty, Zhixiang Chen and Steve Homer* |

| | | **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* |

| | | **Revision of production system rule-bases** - *Patrick M. Murphy and Michael J. Pazzani* |

| | | **Frequencies vs. biases: machine learning problems in natural language processing - abstract** - *F. C. N. Pereira* |

| | | **Learning from Examples with Typed Equational Programming** - *Akira Ishino and Akihiro Yamamoto* |

| | | **Prototype selection using competitive learning** - *Michael Lemmon* |

| | | **A Bayesian framework to integrate symbolic and neural learning** - *Irina Tchoumatchenko and Jean-Gabriel Ganascia* |

| | | **Consideration of risk in reinforcement learning** - *Matthias Heger* |

| | | **To discount or not to discount in reinforcement learning: a case study comparing R learning and Q learning** - *Sridhar Mahadevan* |

| | | **Language Learning from Good Examples** - *Steffen Lange, Jochen Nessel and Rolf Wiehagen* |

| | | **A powerful heuristic for the discovery of complex patterned behavior** - *Raúl E. Valdés-Pérez and Aurora Pérez* |

| | | **Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle** - *Dana Angluin and Donna K. Slonim* |

| | | **Learning an Optimally Accurate Representation System** - *Russell Greiner and Dale Schuurmans* |

| | | **Logic and Learning** - *Daniel N. Osherson, Michael Stob and Scott Weinstein* |

| | | **Higher-Order Neural Networks Applied to 2D and 3D Object Recognition** - *Lilly Spirkovska and Max B. Reid* |

| | | **Incorporating prior knowledge into networks of locally-tuned units** - *Martin Röscheisen, Reimar Hoffman and Volker Tresp* |

| | | **An Introduction to Computational Learning Theory** - *Michael J. Kearns and Umesh V. Vazirani* |

| | | **Refining algorithms with knowledge-based neural networks: improving the Cho-Fasman algorithm for protein folding** - *Richard Maclin and Jude W. Shavlik* |

| | | **Simulating the Child's Acquisition of the Lexicon and Syntax - Experiences with Babel** - *Rick Kazman* |

| | | **A hierarchy of language families learnable by regular language learners** - *Yuji Takada* |

| | | **Efficient Algorithm for Learning Simple Regular Expressions from Noisy Examples** - *Alvis Brāzma* |

| | | **Probability density estimation and local basis function neural networks** - *Padhraic Smyth* |

| | | **Generalized stochastic complexity and its applications to learning** - *Kenji Yamanishi* |

| | | **Learning Unions of Convex Polygons** - *P. Fischer* |

| | | **Incremental abductive EBL** - *William W. Cohen* |

| | | **On Training Simple Neural Networks and Small-weight Neurons** - *T. Hegedüs* |

| | | **An upper bound on the loss from approximate optimal-value functions** - *Satinder P. Singh and Richard C. Yee* |

| | | **Average-Case Analysis of Pattern Language Learning Algorithms** - *Thomas Zeugmann* |

| | | **Learning linear threshold functions in the presence of classification noise** - *T. Bylander* |

| | | **Combining top-down and bottom-up techniques in inductive logic programming** - *John M. Zelle, Raymond J. Mooney and Joshua B. Konvisser* |

| | | **Classification Using Information** - *William I. Gasarch, Mark G. Pleszkoch and Mahendran Velauthapillai* |

| | | **On monotonic strategies for learning r.e.\ languages** - *Sanjay Jain and Arun Sharma* |

| | | **Learning structurally reversible context-free grammars from queries and counterexamples in polynomial time** - *A. Burago* |

| | | **Learning Local and Recognizable ***omega*-languages and Monadic Logic Programs - *A. Saoudi* |

| | | **The minimum description length principle and categorical theories** - *J. R. Quinlan* |

| | | **Learning with instance-based encodings** - *Henry Tirri* |

| | | **Efficient inference of partial types** - *Dexter Kozen, Jens Palsberg and Michael I. Schwartzbach* |

| | | **Efficient distribution-free learning of probabilistic concepts** - *Michael J. Kearns and Robert E. Schapire* |

| | | **On the learnability of discrete distributions** - *M. Kearns, Y. Mansour, D. Ron, R. Rubinfeld, R. Schapire and L. Sellie* |

| | | **Improving generalization with active learning** - *David Cohn, Les Atlas and Richard Ladner* |

| | | **Deductive Plan Generation** - *Wolfgang Bibel and Michael Thielscher* |

| | | **Concept Formation During Interactive Theory Revision** - *Stefan Wrobel* |

| | | **Selective reformulation of examples in concept learning** - *Jean-Daniel Zucker and Jean-Gabriel Ganascia* |

| | | **Inductive inference of recursive concepts** - *Yasuhito Mukouchi* |

| | | **Exploiting random walks for learning** - *P. L. Bartlett, P. Fischer and K.-U. Höffgen* |

| | | **Refutably Probably Approximately Correct Learning** - *Satoshi Matsumoto and Ayumi Shinohara* |

| | | **An incremental learning approach for completable planning** - *Melinda T. Gervasio and Gerald F. DeJong* |

| | | **Acquisition of Children's Addition Strategies: A Model of Impasse-Free, Knowledge-Level Learning** - *Randolph M. Jones and Kurt Vanlehn* |

| | | **On-line learning from search failures** - *Neeraj Bhatnagar and Jack Mostow* |

| | | **Characterization of language learning from informant under various monotonicity constraints** - *S. Lange and T. Zeugmann* |

| | | **Learning without state-estimation in partially observable Markovian decision processes** - *Satinder P. Singh, Tommi Jaakkola and Michael I. Jordan* |

| | | **Matters Horn and Other Features in the Computational Learning Theory Landscape: The Notion of Membership** - *M. Frazier* |

| | | **VC dimension and sampling complexity of learning sparse polynomials and rational functions** - *Marek Karpinski and Thorsten Werther* |

| | | **Learning recursive relations with randomly selected small training sets** - *David W. Aha, Stephanie Lapointe, Charles X. Ling and Stan Matwin* |

| | | **Guest Editorial** - *Katharina Morik, Francesco Bergadano and Wray Buntine* |

| | | **Comparing connectionist and symbolic learning methods** - *J. R. Quinlan* |

| | | **Characterizing language identification by standardizing operations** - *Sanjay Jain and Arun Sharma* |

| | | **Technical note: statistical methods for analyzing speedup learning experiments** - *Oren Etzioni and Ruth Etzioni* |

| | | **Mutual information gaining algorithm and its relation to PAC-learning algorithm** - *Eiji Takimoto, Ichiro Tajika and Akira Maruoka* |

| | | **Associative Reinforcement Learning: Functions in k-DNF** - *Leslie Pack Kaelbling* |

| | | **Co-learnability and FIN-identifiability of enumerable classes of total recursive functions** - *R. Freivalds, Dace Gobleja, Marek Karpinski and Carl H. Smith* |

| | | **Nonuniform learnability** - *Gyora M. Benedek and Alon Itai* |

| | | **The representation of recursive languages and its impact on the efficiency of learning** - *S. Lange* |

| | | **Monotonicity versus Efficiency for Learning Languages from Texts** - *Efim Kinber* |

| | | **Greedy attribute selection** - *Rich Caruana and Dayne Freitag* |

| | | **Derived Sets and Inductive Inference** - *Kalvis Aps\=ıtis* |

| | | **On the Power of Equivalence Queries** - *R. Gavaldà* |

| 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 ***mu*-DNF formulas with malicious membership queries - *Dana Angluin* |

| | | **On Using the Fourier transform to learn disjoint DNF** - *R. Khardon* |

| | | **Optimal Sequential Probability Assignment for Individual Sequences** - *M. J. Weinberger, N. Merhav and M. Feder* |

| 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* |

| 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 | | **Algorithmic Learning Theory, 4th International Workshop on Analogical and Inductive Inference, AII '94, 5th International Workshop on Algorithmic Learning Theory, ALT '94, Reinhardsbrunn Castle, Germany, October 1994, Proceedings** - *Setsuo Arikawa and Klaus P. Jantke* |

| December | | **H**^{infinity} Bounds for the recursive-least-squares algorithm - *B. Hassibi and T. Kailath* |