1998 | | | **Inferring a Rewriting System from Examples** - *Yasuhito Mukouchi, Ikuyo Yamue and Masako Sato* |

| | | **Learning from Examples and Membership Queries with Structured Determinations** - *Prasad Tadepalli and Stuart Russell* |

| | | **Efficient learning of monotone concepts via quadratic optimization** - *David Gamarnik* |

| | | **Lange and Wiehagen's Pattern Language Learning Algorithm: An Average-Case Analysis with respect to its Total Learning Time** - *T. Zeugmann* |

| | | **Transducer-Learning Experiments on Language Understanding** - *David Picó and Enrique Vidal* |

| | | **Polylogarithmic-overhead piecemeal graph exploration** - *Baruch Awerbuch and Stephen G. Kobourov* |

| | | **Hardness results for learning first-order representations and programming by demonstration** - *William W. Cohen* |

| | | **Finite-time regret bounds for the multiarmed bandit problem** - *Nicolò Cesa-Bianchi and Paul Fischer* |

| | | **Strong minimax lower bounds for learning** - *András Antos and Gábor Lugosi* |

| | | **Learning Boxes in High Dimension** - *Amos Beimel and Eyal Kushilevitz* |

| | | **Polynomial-Time Inference of Paralleled Even Monogenic Pure Context-Free Languages** - *Noriyuki Tanida* |

| | | **Structural risk minimization over data-dependent hierarchies** - *J. Shawe-Taylor and P. L. Bartlett* |

| | | **Using learning for approximation in stochastic processes** - *Daphne Koller and Raya Fratkina* |

| | | **Learning with unreliable boundary queries** - *Avrim Blum, Prasad Chalasani, Sally A. Goldman and Donna K. Slonim* |

| | | **On Bayes Methods for On-Line Boolean Prediction** - *Nicolò Cesa-Bianchi, David P. Helmbold and Sandra Panizza* |

| | | **Learning k-Variable Pattern Languages Efficiently Stochastically Finite on Average from Positive Data** - *Peter Rossmanith and Thomas Zeugmann* |

| | | **Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms** - *Thomas G. Dietterich* |

| | | **Learning recursive languages from good examples** - *Steffen Lange, Jochen Nessel and Rolf Wiehagen* |

| | | **Minimal Concept Identification and Reliability** - *Sanjay Jain* |

| | | **Characterizing Sufficient Expertise for Learning System Validation** - *Gunter Grieser, Klaus P. Jantke and Steffen Lange* |

| | | **Fast Online Q(***lambda*) - *Marco Wiering and Jürgen Schmidhuber* |

| | | **Learning to Win Process-Control Games Watching Game-Masters** - *J. Case, M. Ott, A. Sharma and F. Stephan* |

| | | **On sequential prediction of individual sequences relative to a set of experts** - *Nicolò Cesa-Bianchi and Gábor Lugosi* |

| | | **Elements of Scientific Inquiry** - *Eric Martin and Daniel N. Osherson* |

| | | **An efficient boosting algorithm for combining preferences** - *Yoav Freund, Raj Iyer, Robert E. Schapire and Yoram Singer* |

| | | **Uniform Characterizations of polynomial-query learnabilities** - *Yosuke Hayashi, Satoshi Matsumoto, Ayumi Shinohara and Masayuki Takeda* |

| | | **PAC Learning from Positive Statistical Queries** - *François Denis* |

| | | **On the boosting algorithm for multiclass functions based on information-theoretic criterion for approximation** - *Eiji Takimoto and Akira Maruoka* |

| | | **Predictive Learning Models for Concept Drift** - *John Case, Sanjay Jain, Susanne Kaufmann, Arun Sharma and Frank Stephan* |

| | | **Using Attribute Grammars for Description of Inductive Inference Search Space** - *Uğis Sarkans and J. Bārzdiņs* |

| | | **Learning in the 'real world'** - *Lorenzo Saitta and Filippo Neri* |

| | | **Learning to communicate via unknown channel** - *Meir Feder* |

| | | **Noise-tolerant distribution-free learning of general geometric concepts** - *Nader H. Bshouty, Sally A. Goldman, H. David Mathias, Subhash Suri and Hisao Tamaki* |

| | | **Learning solution preferences in constraint problems** - *Francesca Rossi and Alessandro Sperduti* |

| | | **Finding a One-Variable Pattern from Incomplete Data** - *Hiroshi Sakamoto* |

| | | **Neural networks and efficient associative memory** - *Matthias Miltrup and Georg Schnitger* |

| | | **Identifying nearly minimal Gödel numbers from additional information** - *Rusins Freivalds, Ognian Botuscharov and Rolf Wiehagen* |

| | | **Consistent Polynomial Identification in the Limit** - *Werner Stein* |

| | | **Using Computational Learning Strategies as a Tool for Combinatorial Optimization** - *Andreas Birkendorf and Hans-Ulrich Simon* |

| | | **A supra-classifier architecture for scalable knowledge reuse** - *Kurt D. Bollacker and Joydeep Ghosh* |

| | | **Learnability of a subclass of extended pattern languages** - *Andrew R. Mitchell* |

| | | **Bayesian classifiers are large margin hyperplanes in a Hilbert space** - *Nello Cristianini, John Shawe-Taylor and Peter Sykacek* |

| | | **Property testing and its connection to learning and approximation** - *Oded Goldreich, Shari Goldwasser and Dana Ron* |

| | | **Improving text classification by shrinkage in a hierarchy of classes** - *Andrew McCallum, Ronald Rosenfeld, Tom Mitchell and Andrew Y. Ng* |

| | | **An experimental evaluation of coevolutive concept learning** - *Cosimo Anglano, Attilio Giordana, Giuseppe Lo Bello and Lorenza Saitta* |

| | | **Applying grammatical inference in learning a language model for oral dialogue** - *Jacques Chodorowski and Laurent Miclet* |

| | | **The query complexity of finding local minima in the lattice** - *Amos Beimel, Felix Geller and Eyal Kushilevitz* |

| | | **Tracking the best regressor** - *Mark Herbster and Manfred K. Warmuth* |

| | | **Learning Coordination Strategies for Cooperative Multiagent Systems** - *F. Ho and M. Kamel* |

| | | **Combining nearest neighbor classifiers through multiple feature subsets** - *Stephen D. Bay* |

| | | **Conjectural Equilibrium in Multiagent Learning** - *Michael P. Wellman and Junling Hu* |

| | | **Learning with restricted focus of attention** - *Shai Ben-David and Eli Dichterman* |

| | | **Classification Accuracy Based on Observed Margin** - *John Shawe-Taylor* |

| | | **Teaching an agent to test students** - *Gheorghe Tecuci and Harry Keeling* |

| | | **Srtuctural machine learning with Galois lattice and graphs** - *Michel Liquiere and Jean Sallantin* |

| | | **On the sample complexity of learning functions with bounded variation** - *Philip M. Long* |

| | | **Learning deterministic finite automaton with a recurrent neural network** - *Laura Firoiu, Tim Oates and Paul R. Cohen* |

| | | **The case against accuracy estimation for comparing induction algorithms** - *Foster Provost, Tom Fawcett and Ron Kohavi* |

| | | **Relational reinforcement learning** - *Sašo Džeroski, Luc De Raedt and Hendrik Blockeel* |

| | | **Refining initial points for K-Means clustering** - *Paul S. Bradley and Usama M. Fayyad* |

| | | **Exact learning of tree patterns from queries and counterexamples** - *Thomas R. Amoth, Paul Cull and Prasad Tadepalli* |

| | | **An information-theoretic definition of similarity** - *Dekang Lin* |

| | | **Covering cubes by random half cubes, with applications to binary neural networks** - *Jeong Han Kim and James R. Roche* |

| | | **Top-down induction of clustering trees** - *Hendrik Blockeel, Luc De Raedt and Jan Ramon* |

| | | **On-line learning with malicious noise and the closure algorithm** - *Peter Auer and Nicolò Cesa-Bianchi* |

| | | **Real language learning** - *Jerome A. Feldman* |

| | | **Lower Bounds for the Complexity of Learning Half-Spaces with Membership Queries** - *Valery N. Shevchenko and Nikolai Yu. Zolotykh* |

| | | **A neural network model for prognostic prediction** - *W. Nick Street* |

| | | **A Decision-Theoretic Extension of Stochcastic Complexity and Its Applications to Learning** - *Kenji Yamanishi* |

| | | **Q2: memory-based active learning for optimizing noisy continuous functions** - *Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan and Mary S. Lee* |

| | | **Collaborative filtering using weighted majority prediction algorithms** - *Atsuyoshi Nakamura and Naoki Abe* |

| | | **Locality, Reversibility, and Beyond: Learning Languages from Positive Data** - *Tom Head, Satoshi Kobayashi and Takashi Yokomori* |

| | | **Learning from Expert Hypotheses and Training Examples** - *Shigeo Kaneda, Hussein Almuallim, Yasuhiro Akiba and Megumi Ishi* |

| | | **Editors' Introduction** - *Michael M. Richter, Carl H. Smith, Rolf Wiehagen and Thomas Zeugmann* |

| | | **Learning Matrix Functions over Rings** - *Nader H. Bshouty, Christino Tamon and David K. Wilson* |

| | | **Learning first order universal Horn expressions** - *Roni Khardon* |

| | | **Learning with Refutation** - *Sanjay Jain* |

| | | **Localization vs. Identification of Semi-Algebraic Sets** - *Shai Ben-David and Michael Lindenbaum* |

| | | **A stochastic search approach to grammar induction** - *Hugues Juillé and Jordan B. Pollack* |

| | | **Results of the Abbadingo one DFA learning competition and a new evidence-driven state merging algorithm** - *Kevin J. Lang, Barak A. Pearlmutter and Rodney A. Price* |

| | | **Co-Evolution in the Successful Learning of Backgammon Strategy** - *Jordan B. Pollack and Alan D. Blair* |

| | | **Learning to locate an object in 3D space from a sequence of camera images** - *Dimitris Margaritis and Sebastian Thrun* |

| | | **Multistrategy learning for information extraction** - *Dayne Freitag* |

| | | **Robust Sensor Fusion: Analysis and Application to Audio Visual Speech Recognition** - *Javier R. Movellan and Paul Mineiro* |

| | | **Choice of Basis for Laplace Approximation** - *David J. C. MacKay* |

| | | **Comparing the Power of Probabilistic Learning and Oracle Identification under Monotonicity Constraints** - *Léa Meyer* |

| | | **On the power of learning robustly** - *Sanjay Jain, Carl Smith and Rolf Wiehagen* |

| | | **Learning Classification Programs: The Genetic Algorithm Approach** - *Attilio Giordana and Giuseppe Lo Bello* |

| | | **Learning a subclass of linear languages from positive structural information** - *José M. Sempere and G. Nagaraja* |

| | | **Individual learning of coordination knowledge** - *Sandip Sen and Mahendra Sekaran* |

| | | **A game of prediction with expert advice** - *V. Vovk* |

| | | **The Data Driven Approach Applied to the OSTIA Algorithm** - *José Oncina* |

| | | **Combining labeled and unlabeled data with co-training** - *Avrim Blum and Tom Mitchell* |

| | | **Near-optimal reinforcement learning in polynomial time** - *Michael Kearns and Satinder Singh* |

| | | **A note on batch and incremental learnability** - *Arun Sharma* |

| | | **On Learning Read-k-Satisfy-j DNF** - *Howard Aizenstein, Avrim Blum, Roni Khardon, Eyal Kushilevitz, Leonard Pitt and Dan Roth* |

| | | **On the power of decision lists** - *Richard Nock and Pascal Jappy* |

| | | **Can Finite Samples Detect Singularities of Real-Valued Functions?** - *Shai Ben-David* |

| | | **PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples** - *Philip M. Long and Lei Tan* |

| | | **Proceedings of the Eleventh Annual Conference on Computational Learning Theory** - *Peter Bartlett and Yishay Mansour* |

| | | **Minimax relative loss analysis for sequential prediction algorithms using parametric hypotheses** - *Kenji Yamanishi* |

| | | **Intra-option learning about temporally abstract actions** - *Richard S. Sutton, Doina Precup and Satinder Singh* |

| | | **Value function based production scheduling** - *Jeff G. Schneider, Justin A. Boyan and Andrew W. Moore* |

| | | **Constructing predicate mappings for goal-dependent abstraction** - *Yoshiaki Okubo and Makoto Haraguchi* |

| | | **Learning Algebraic Structures from Text Using Semantical Knowledge** - *Frank Stephan and Yuri Ventsov* |

| | | **Learning one-variable pattern languages in linear average time** - *Rüdiger Reischuk and Thomas Zeugmann* |

| | | **Synthesizing Learners Tolerating Computable Noisy Data** - *John Case and Sanjay Jain* |

| | | **Learning collaborative information filters** - *Daniel Billsus and Michael J. Pazzani* |

| | | **Solving a huge number of similar tasks: a combination of multi-task learning and a hierarchical Bayesian approach** - *Tom Heskes* |

| | | **Learning agents for uncertain environments** - *Stuart Russell* |

| | | **Grammar Model and Grammar Induction in the System NL PAGE** - *Vlado Kešelj* |

| | | **Cryptographic Limitations on Parallelizing Membership and Equivalence Queries with Applications to Random Self-Reductions** - *Marc Fischlin* |

| | | **Using a permutation test for attribute selection in decision trees** - *Eibe Frank and Ian H. Witten* |

| | | **Using symbol clustering to improve probabilistic automaton inference** - *Pierre Dupont and Lin Chase* |

| | | **Learning Unary Output Two-Tape Automata from Multiplicity and Equivalence Queries** - *Giovanna Melideo and Stefano Varricchio* |

| | | **Using communication to reduce locality in distributed multiagent learning** - *Maja J. Mataric* |

| | | **Finding tree patterns consistent with positive and negative examples using queries** - *Hiroki Ishizaka, Hiroki Arimura and Takeshi Shinohara* |

| | | **A process-oriented heuristic for model selection** - *Pedro Domingos* |

| | | **Self bounding learning algorithms** - *Yoav Freund* |

| | | **A performance evaluation of automatic survey classifiers** - *P. Viechnicki* |

| | | **On restricted-focus-of-attention learnability of Boolean functions** - *Andreas Birkendorf, Eli Dichterman, Jeffrey Jackson, Norbert Klasner and Hans Ulrich Simon* |

| | | **Learnability of Translations from Positive Examples** - *Noriko Sugimoto* |

| | | **Generalization and specialization strategies for learning r.e. languages** - *Sanjay Jain and Arun Sharma* |

| | | **Improved lower bounds for learning from noisy examples: an information-theoretic approach** - *Claudio Gentile and David P. Helmbold* |

| | | **Multi-criteria reinforcement learning** - *Zoltán Gábor, Zsolt Kalmár and Csaba Szepesvári* |

| | | **Scalability Issues in Inductive Logic Programming** - *Stefan Wrobel* |

| | | **Meaning helps learning syntax** - *Isabelle Tellier* |

| | | **Employing EM and pool-based active learning for text classification** - *Andrew Kachites McCallum and Kamal Nigam* |

| | | **Evolving structured programs with hierarchical instructions and skip nodes** - *Rafał Sałustowicz and Jürgen Schmidhuber* |

| | | **Feature selection via concave minimization and support vector machines** - *Paul S. Bradley and Olvi L. Mangasarian* |

| | | **Relative Sizes of Learnable Sets** - *Lance Fortnow, Rīsiņs Freivalds, William I. Gasarch, Martin Kummer, Stuart A. Kurtz, Carl H. Smith and Frank Stephan* |

| | | **A learning rate analysis of reinforcement learning algorithms in finite-horizon** - *Frédérick Garcia and Seydina M. Ndiaye* |

| | | **Learning first-order acyclic Horn programs from entailment** - *Chandra Reddy and Prasad Tadepalli* |

| | | **Stochastic resonance with adaptive fuzzy systems** - *Sanya Mitaim and Bart Kosko* |

| | | **Exact Learning of Discretized Geometric Concepts** - *Nader H. Bshouty, Paul W. Goldberg, Sally A. Goldman and H. David Mathias* |

| | | **Bayesian Landmark Learning for Mobile Robot Localization** - *Sebastian Thrun* |

| | | **Tracking the Best Disjunction** - *Peter Auer and Manfred K. Warmuth* |

| | | **Cross-validation for binary classification by real-valued functions: theoretical analysis** - *Martin Anthony and Sean B. Holden* |

| | | **Logical Aspects of Several Bottom-Up Fittings** - *Akihiro Yamamoto* |

| | | **Query learning strategies using boosting and bagging** - *Naoki Abe and Hiroshi Mamitsuka* |

| | | **Structured Weight-Based Prediction Algorithms** - *Akira Maruoka and Eiji Takimoto* |

| | | **Computational Aspects of Parallel Attribute-Efficient Learning** - *Peter Damaschke* |

| | | **Learning a subclass of context-free languages** - *J. D. Emerald, K. G. Subramanian and D. G. Thomas* |

| | | **A note on learning from multiple-instance examples** - *Avrim Blum and Adam Kalai* |

| | | **Classification using ***Phi*-machines and constructive function approximation - *Doina Precup and Paul E. Utgoff* |

| | | **Coevolutionary learning: a case study** - *Hugues Juille and Jordan B. Pollack* |

| | | **Pattern discovery in biosequences** - *Alvis Brāzma, Inge Jonassen, Jaak Vilo and Esko Ukkonen* |

| | | **A Comparison of Identification Criteria for Inductive Inference of Recursive Real-Valued Functions** - *Eiju Hirowatari and Setsuo Arikawa* |

| | | **On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion** - *Alex J. Smola and Bernhard Schölkopf* |

| | | **Pharmacophore discovery using the inductive logic programming system PROG0L** - *Paul Finn, Stephen Muggleton, David Page and Ashwin Srinivasan* |

| | | **On the Sample Complexity for Neural Trees** - *Michael Schmitt* |

| | | **How considering incompatible state mergins may reduce the DFA induction search tree** - *François Coste and Jacques Nicolas* |

| | | **Learning regular grammars to model music style: comparing different coding schemes** - *Pedro P. Cruz-Alcázar and Enrique Vidal-Ruiz* |

| | | **Guest editor's foreword** - *Robert E. Schapire* |

| | | **Aspects of complexity of conservative probabilistic learning** - *Léa Meyer* |

| | | **Elevator Group Control Using Multiple Reinforcement Learning Agents** - *Robert H. Crites and Andrew G. Barto* |

| | | **A Class of Asymptotically Stable Algorithms for Learning-Rate Adaptation** - *S. M. Rüger* |

| | | **Learning to drive a bicycle using reinforcement learning and shaping** - *Jette Randløv and Preben Alstrøm* |

| | | **On the learnability and usage of acyclic probabilistic finite automata** - *Dana Ron, Yoram Singer and Naftali Tishby* |

| | | **Bayesian network classification with continuous attributes: getting the best of both discretization and parametric fitting** - *Nir Friedman, Moises Goldszmidt and Thomas J. Lee* |

| | | **Efficient distribution-free population learning of simple concepts** - *Atsuyoshi Nakamura, Jun-ichi Takeuchi and Naoki Abe* |

| | | **Characteristic Sets for Unions of Regular Pattern Languages and Compactness** - *Masako Sato, Yasuhito Mukouchi and Dao Zheng* |

| | | **Knowledge-based learning in exploratory science: learning rules to predict rodent carcinogenicity** - *Yongwon Lee, Bruce G. Buchanan and John M. Aronis* |

| | | **Heading in the right direction** - *Hagit Shatkay and Leslie P. Kaelbling* |

| | | **Universal portfolio selection** - *V. Vovk and C. Watkins* |

| | | **A randomized ANOVA procedure for comparing performance curves** - *Justus H. Piater, Paul R. Cohen, Xiaoqin Zhang and Michael Atighetchi* |

| | | **Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed Learning** - *Shai Ben-David and Nadav Eiron* |

| | | **The Kernel-Adatron algorithm: a fast and simple learning procedure for Support Vector machines** - *Thilo-Thomas Frieß, Nello Cristianini and Colin Campbell* |

| | | **Lime: A System for Learning Relations** - *Eric McCreath and Arun Sharma* |

| | | **KnightCap: a chess program that learns by combining TD(***lambda*) with game-tree search - *Jonathan Baxter, Andrew Trigdell and Lex Weaver* |

| | | **Using eligibility traces to find the best memoryless policy in partially observable Markov decision processes** - *John Loch and Satinder Singh* |

| | | **An investigation of transformation-based learning in discourse** - *Ken Samuel, Sandra Carberry and K. Vijay-Shanker* |

| | | **How to learn an unknown environment. I: the rectilinear case** - *Xiaotie Deng, Tiko Kameda and Christos Papadimitriou* |

| | | **A new view of the EM algorithm that justifies incremental, sparse and other variants** - *R. M. Neal and G. E. Hinton* |

| | | **Learning sorting and decision trees with POMDPs** - *Blai Bonet and Héctor Geffner* |

| | | **Machine learning for the detection of oil spills in satellite radar images** - *Miroslav Kubat, Robert C. Holte and Stan Matwin* |

| | | **Genetic programming and deductive-inductive learning: a multi-strategy approach** - *Ricardo Aler, Daniel Borrajo and Pedro Isasi* |

| | | **Minimizing alpha-Information for Generalization and Interpretation** - *R. Kamimura* |

| | | **Locally threshold testable languages in strict sense: Application to the inference problem** - *José Ruiz, Salvador España and Pedro Garciá* |

| | | **A learning model for oscillatory networks** - *Jun Nishii* |

| | | **Towards the Validation of Inductive Learning Systems** - *Gunter Grieser, Klaus P. Jantke and Steffen Lange* |

| | | **Stochastic inference of regular tree languages** - *Rafael C. Carrasco, Jose Oncina and Jorge Calera* |

| | | **Learning stochastic finite automata from experts** - *Colin de la Higuera* |

| | | **Multiple-instance learning for natural scene classification** - *Oded Maron and Aparna Lakshmi Ratan* |

| | | **Practical algorithms for on-line sampling** - *Carlos Domingo, Ricard Gavaldà and Osamu Watanabe* |

| | | **Discovery of Differential Equations from Numerical Data** - *K. Niijima, H. Uchida, E. Hirowatari and S. Arikawa* |

| | | **Strategy Under the Unknown Stochastic Environment: The Nonparametric Lob-Pass Problem** - *K. Hiraoka and S. Amari* |

| | | **RL-TOPs: an architecture for modularity and re-use in reinforcement learning** - *Malcolm R. K. Ryan and Mark D. Pendrith* |

| | | **Theory refinement for Bayesian networks with hidden variables** - *Sowmya Ramachandran and Raymond J. Mooney* |

| | | **Classification using information** - *William Gasarch, Mark G. Pleszkoch, Frank Stephan and Mahendran Velauthapillai* |

| | | **A polynomial time incremental algorithm for learning DFA** - *Rajes Parekh, Codrin Nichitiu and Vasant Honavar* |

| | | **On Variants of Iterative Learning** - *Steffen Lange and Gunter Grieser* |

| | | **Testing problems with sub-learning sample complexity** - *Michael Kearns and Dana Ron* |

| | | **Approximate learning of random subsequential transducers** - *Antonio Castellanos* |

| | | **Learning atomic formulas with prescribed properties** - *Irene Tsapara and György Turán* |

| | | **Attribute-efficient learning in query and mistake-bound models** - *Nader Bshouty and Lisa Hellerstein* |

| | | **Learning fuzzy decision trees** - *Bruno Apolloni, Giacomo Zamponi and Anna Maria Zanaboni* |

| | | **Well-behaved Borgs, Bolos, and Berserkers** - *Diana F. Gordon* |

| | | **Grammatical inference in document recognition** - *Alexander S. Saidi and Souad Tayeb-bey* |

| | | **Analysis of Case-Based Representability of Boolean Functions by Monotone Theory** - *Ken Satoh* |

| | | **Learning the grammar of dance** - *Joshua M. Stuart and Elizabeth Bradley* |

| | | **Identification of noisy linear systems with discrete random input** - *E. Gassiat and E. Gautherat* |

| | | **Convergence Rate of Minimization Learning for Neural Networks** - *M. H. Mohamed, T. Minamoto and K. Niijima* |

| | | **An analysis of direct reinforcement learning in non-Markovian domains** - *Mark D. Pendrith and Michael J. McGarity* |

| | | **Investigations on Measure-one Identification of Classes of Languages** - *Franco Montagna* |

| | | **Birds can fly...** - *Jochen Nessel* |

| | | **A Good Oracle Is Hard to Beat** - *Douglas A. Cenzer and William R. Moser* |

| | | **Colearning in Differential Games** - *John W. Sheppard* |

| | | **Closedness Properties in EX-identification of Recursive Functions** - *K. Aps\=ıtis, R. Freivalds, R. Simanovskis and J. Smotrovs* |

| | | **Tracking the Best Expert** - *Mark Herbster and Manfred Warmuth* |

| | | **Belief revision in the service of scientific discovery** - *Eric Martin and Daniel N. Osherson* |

| | | **An analysis of actor/critic algorithms using eligibility traces: reinforcement learning with imperfect value functions** - *Hajime Kimura and Shigenobu Kobayashi* |

| | | **A case study in the use of theory revision in requirements validation** - *T. L. McCluskey and M. M. West* |

| | | **A fast, bottom-up decision tree pruning algorithm with near-optimal generalization** - *Michael Kearns and Yishay Mansour* |

| | | **A Polynomial-Time Algorithm for Learning Noisy Linear Threshold Functions** - *Avrim Blum, Alan M. Frieze, Ravi Kannan and Santosh Vempala* |

| | | **Learning to recognize volcanoes on Venus** - *Michael C. Burl, Lars Asker, Padhraic Smyth, Usama Fayyad, Pietro Perona, Larry Crumpler and Jayne AubeIe* |

| | | **PAC Learning Intersections of Halfspaces with Membership Queries** - *Stephen Kwek and Leonard Pitt* |

| | | **Multiagent reinforcement learning: theoretical framework and an algorithm** - *Junling Hu and Michael P. Wellman* |

| | | **Statistical Mechanics of Online Learning of Drifting Concepts: A Variational Approach** - *Renato Vicente, Osame Kinouchi and Nestor Caticha* |

| | | **Learning a language-independent representation for terms from a partially aligned corpus** - *Michael L. Littman, Fan Jiang and Greg A. Keim* |

| | | **Learning from Entailment of Logic Programs with Local Variables** - *M. R. K. Krishna Rao and A. Sattar* |

| | | **Comments on "Co-Evolution in the Successful Learning of Backgammon Strategy"** - *Gerald Tesauro* |

| | | **Generating accurate rule sets without global optimization** - *Eibe Frank and Ian H. Witten* |

| | | **Sequential prediction of individual sequences under general loss functions** - *D. Haussler, J. Kivinen and M. K. Warmuth* |

| | | **Learning Sub-classes of Monotone DNF on the Uniform Distribution** - *Karsten A. Verbeurgt* |

| | | **Local cascade generalization** - *João Gama* |

| | | **Automatic segmentation of continuous trajectories with invariance to nonlinear warpings of time** - *Lawrence K. Saul* |

| | | **The MAXQ method for hierarchical reinforcement learning** - *Thomas G. Dietterich* |

| | | **Prequential and Cross-Validated Regression Estimation** - *Dharmendra S. Modha and Elias Masry* |

| | | **Prediction, learning, uniform convergence, and scale-sensitive dimensions** - *Peter L. Bartlett and Philip M. Long* |

| | | **Specification and simulation of statistical query algorithms for efficiency and noise tolerance** - *Javed A. Aslam and Scott E. Decatur* |

| | | **On feature selection: learning with exponentially many irrelevant features as training examples** - *Andrew Y. Ng* |

| | | **Ridge regression learning algorithm in dual variables** - *G. Saunders, A. Gammerman and V. Vovk* |

| | | **A Fast Algorithm for Discovering Optimal String Patterns in Large Text Databases** - *Hiroki Arimura, Atsushi Wataki, Ryoichi Fujino and Setsuo Arikawa* |

| | | **Approximating hyper-rectangles: Learning and pseudorandom sets** - *Peter Auer, Philip M. Long and Aravind Srinivasan* |

| | | **Learning to Improve Coordinated Actions in Cooperative Distributed Problem-Solving** - *Toshiharu Sugawara and Victor Lesser* |

| | | **Extracting Hidden Context** - *Michael Bonnell Harries, Claude Sammut and Kim Horn* |

| | | **The problem with noise and small disjuncts** - *Gary M. Weiss and Haym Hirsh* |

| August | | **Analyzing the Average-Case Behavior of Conjunctive Learning Algorithms** - *R. Reischuk and T. Zeugmann* |

| October | | **Algorithmic Learning Theory, 9th International Conference, ALT '98, Otzenhausen, Germany, October 1998, Proceedings** - *Michael M. Richter and Carl H. Smith and Rolf Wiehagen and Thomas Zeugmann* |