1999 | | | **Piecemeal Graph Exploration by a Mobile Robot** - *Baruch Awerbuch, Margrit Betke, Ronald L. Rivest and Mona Singh* |

| | | **Large Margin Classification Using the Perceptron Algorithm** - *Yoav Freund and Robert E. Schapire* |

| | | **On Learning Functions from Noise-Free and Noisy Samples via Occam's Razor** - *B. Natarajan* |

| | | **The complexity of universal text-learners** - *F. Stephan and S. A. Terwijn* |

| | | **Computational Sample Complexity** - *Scott E. Decatur, Oded Goldreich and Dana Ron* |

| | | **Learning Multiplicity Automata from Smallest Counterexamples** - *Jürgen Forster* |

| | | **Learning user evaluation functions for adaptive scheduling assistance** - *Melinda T. Gervasio, Wayne Iba and Pat Langley* |

| | | **The synthesis of language learners** - *Ganesh R. Baliga, John Case and Sanjay Jain* |

| | | **Individual sequence prediction - upper bounds and application for complexity** - *Chamy Allenberg* |

| | | **Additive models, boosting, and inference for generalized divergences** - *John Lafferty* |

| | | **On the Asymptotic Behaviour of a Constant Stepsize Temporal-Difference Learning Algorithm** - *Vladislav Tadic* |

| | | **Proceedings of the Twelfth Annual Conference on Computational Learning Theory** - *Shai Ben-David and Phil Long* |

| | | **Learning from Random Text** - *Peter Rossmanith* |

| | | **The VC-Dimension of Subclasses of Pattern Languages** - *Andrew Mitchell, Tobias Scheffer, Arun Sharma and Frank Stephan* |

| | | **The robustness of the p-norm algorithms** - *Claudio Gentile and Nick Littlestone* |

| | | **A Complete and Tight Average-Case Analysis of Learning Monomials** - *Rüdiger Reischuk and Thomas Zeugmann* |

| | | **Forgetting Exceptions is Harmful in Language Learning** - *Walter Daelemans, Antal van den Bosch and Jakub Zavrel.* |

| | | **An On-Line Prediction Algorithm Combining Several Prediction Strategies in the Shared Bet Model** - *Ichiro Tajika, Eiji Takimoto and Akira Maruoka* |

| | | **Instance-family abstraction in memory-based language learning** - *Antal van den Bosch* |

| | | **Guest Editors' Introduction** - *Philip K. Chan, Salvatore J. Stolfo and David Wolpert* |

| | | **Learning Function-Free Horn Expressions** - *Roni Khardon* |

| | | **Avoiding Coding Tricks by Hyperrobust Learning** - *Matthias Ott and Frank Stephan* |

| | | **The Power of Vacillation in Language Learning** - *John Case* |

| | | **A Winnow-Based Approach to Context-Sensitive Spelling Correction** - *Andrew R. Golding and Dan Roth* |

| | | **More efficient PAC-learning of DNF with membership queries under the uniform distribution** - *Nader H. Bshouty, Jeffrey C. Jackson and Christino Tamon* |

| | | **Decision Trees: Old and New Results** - *R. Fleischer* |

| | | **Some PAC-Bayesian Theorems** - *David A. McAllester* |

| | | **Hierarchical optimization of policy-coupled semi-Markov decision processes** - *Gang Wang and Sridhar Mahadevan* |

| | | **What can we learn from the web?** - *William W. Cohen* |

| | | **Flattening and Implication** - *Kouichi Hirata* |

| | | **Feature selection as a preprocessing step for hierarchical clustering** - *Luis Talavera* |

| | | **A Principal Components Approach to Combining Regression Estimates** - *Christopher J. Merz and Michael J. Pazzani* |

| | | **Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: Basic Properties** - *Joe Suzuki* |

| | | **Theoretical analysis of a class of randomized regularization methods** - *Tong Zhang* |

| | | **Estimating a mixture of two product distributions** - *Yoav Freund and Yishay Mansour* |

| | | **Combining statistical learning with a knowledge-based approach - a case study in intensive care monitoring** - *Katharina Morik, Peter Brockhausen and Thorsten Joachims* |

| | | **A hybrid lazy-eager approach to reducing the computation and memory requirements of local parametric learning algorithms** - *Yuanhui Zhou and Carla Brodley* |

| | | **A Simulated Annealing-Based Learning Algorithm for Boolean DNF** - *Andreas Alexander Albrecht and Kathleen Steinhöfel* |

| | | **On the learnability of rich function classes** - *J. Ratsaby and V. Maiorov* |

| | | **Hardness Results for Neural Network Approximation Problems** - *Peter L. Bartlett and Shai Ben-David* |

| | | **A PTAS for Clustering in Metric Spaces** - *Piotr Indyk* |

| | | **Implicit imitation in multiagent reinforcement learning** - *Bob Price and Craig Boutilier* |

| | | **Inductive Inference with Procrastination: Back to Definitions** - *Andris Ambainis, Rusins Freivalds and Carl H. Smith* |

| | | **Expected error analysis for model selection** - *Tobias Scheffer and Thorsten Joachims* |

| | | **A region-based learning approach to discovering temporal structures in data** - *Wei Zhang* |

| | | **Similarity-Based Models of Word Cooccurrence Probabilities** - *Ido Dagan, Lillian Lee and Fernando C. N. Pereira* |

| | | **Learning to Order Things** - *W. W. Cohen, R. E. Schapire and Y. Singer* |

| | | **The Complexity of Learning According to Two Models of a Drifting Environment** - *Philip M. Long* |

| | | **On the Strength of Incremental Learning** - *Steffen Lange and Gunter Grieser* |

| | | **Boolean Formulas are Hard to Learn for Most Gate Bases** - *Victor Dalmau* |

| | | **On Learning Unions of Pattern Languages and Tree Patterns** - *Sally A. Goldman and Stephen S. Kwek* |

| | | **Feature selection for unbalanced class distribution and Naive Bayes** - *Dunja Mladenić and Marko Grobelnik* |

| | | **Improving support vector machine classifiers by modifying kernel functions** - *S. Amari and S. Wu* |

| | | **Extensional set learning** - *Sebastiaan A. Terwijn* |

| | | **AdaCost: misclassification cost-sensitive boosting** - *Wei Fan, Salvatore J. Stolfo, Junxin Zhang and Philip K. Chan* |

| | | **Averaging Expert Predictions** - *Jyrki Kivinen and Manfred K. Warmuth* |

| | | **Theoretical Views of Boosting and Applications** - *Robert E. Schapire* |

| | | **ACT-R and learning** - *John R. Anderson* |

| | | **Algebraic Analysis for Singular Statistical Estimation** - *Sumio Watanabe* |

| | | **Generalization Error of Linear Neural Networks in Unidentifiable Cases** - *Kenji Fukumizu* |

| | | **Complexity in the Case Against Accuracy: When Building One Function-Free Horn Clause is as Hard as Any** - *Richard Nock* |

| | | **Universal Portfolios With and Without Transaction Costs** - *Avrim Blum and Adam Kalai* |

| | | **A Method of Similarity-Driven Knowledge Revision for Type Specification** - *Nobuhiro Morita, Makoto Haraguchi and Yoshiaki Okubo* |

| | | **An Application of Codes to Attribute-Efficient Learning** - *Thomas Hofmeister* |

| | | **A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns** - *Sally A. Goldman and Stephen D. Scott* |

| | | **An Introduction to Variational Methods for Graphical Models** - *Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola and Lawrence K. Saul* |

| | | **Learning threshold functions with small weights using membership queries** - *Elias Abboud, Nader Agha, Nader H. Bshouty, Nizar Radwan and Fathi Saleh* |

| | | **Lazy Bayesian rules: a lazy semi-naive Bayesian learning technique competitive to boosting decision trees** - *Zijian Zheng, Geoffrey I. Webb and Kai Ming Ting* |

| | | **Hierarchical models for screening iron deficiency anemia** - *Igor V. Cadez, Christine E. McLaren, Padhraic Smyth and Geoffrey J. McLachlan* |

| | | **A Constant-Factor Approximation Algorithm for the k-Median Problem (Extended Abstract)** - *Moses Charikar, Sudipto Guha, Eva Tardos and David B. Shmoys* |

| | | **An Experimental Evaluation of Integrating Machine Learning with Knowledge** - *Geoffrey I. Webb, Jason Wells and Zijian Zheng* |

| | | **Noise-tolerant recursive best-first induction** - *Uroš Pompe* |

| | | **Introducing the Special Issue of Machine Learning Selected from Papers Presented at the 1997 Conference on Computational Learning Theory, COLT'97** - *John Shawe-Taylor* |

| | | **Positive and Unlabeled Examples Help Learning** - *Francesco De Comité, François Denis, Remi Gilleron and Fabien Letouzey* |

| | | **An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery** - *Michael R. Brent* |

| | | **The More We Learn the Less We Know? On Inductive Learning from Examples** - *Piotr Ejdys and Grzegorz Gára* |

| | | **Combining error-driven pruning and classification for partial parsing** - *Claire Cardie, Scott Mardis and David Pierce* |

| | | **Approximation algorithms for clustering problems** - *David B. Shmoys* |

| | | **Minimum Generalization Via Reflection: A Fast Linear Threshold Learner** - *Steven Hampson and Dennis Kibler* |

| | | **Finding Relevant Variables in PAC Model with Membership Queries** - *Jun Tarui David Guijarro and Tatsuie Tsukiji* |

| | | **General and Efficient Multisplitting of Numerical Attributes** - *Tapio Elomaa and Juho Rousu* |

| | | **Approximate ILP Rules by Backpropagation Neural Network: A Result on Thai Character Recognition** - *Boonserm Kijsirikul and Sukree Sinthupinyo* |

| | | **An apprentice learning model** - *Stephen S. Kwek* |

| | | **PAC Learning with Nasty Noise** - *Nader H. Bshouty, Nadav Eiron and Eyal Kushilevitz* |

| | | **On learning in the presence of unspecified attribute values** - *Nader H. Bshouty and David K. Wilson* |

| | | **Extension of the PAC framework to finite and countable Markov chains** - *David Gamarnik* |

| | | **Structural Results About On-line Learning Models With and Without Queries** - *Peter Auer and Philip M. Long* |

| | | **General Linear Relations among Different Types of Predictive Complexity** - *Yuri Kalnishkan* |

| | | **Attribute dependencies, understandability and split selection in tree based models** - *Marko Robnik-ťikonja and Igor Kononenko* |

| | | **Reinforcement learning and mistake bounded algorithms** - *Yishay Mansour* |

| | | **Policy invariance under reward transformations: theory and application to reward shaping** - *Andrew Y. Ng, Daishi Harada and Stuart Russell* |

| | | **Learning hierarchical performance knowledge by observation** - *Michael van Lent and John Laird* |

| | | **Learning Range Restricted Horn Expressions** - *Roni Khardon* |

| | | **Predicting Nearly as Well as the Best Pruning of a Planar Decision Graph** - *Eiji Takimoto and Manfred K. Warmuth* |

| | | **Deciding the Vapnik-Cervonenkis dimension is ***Sigma*^{p}_{3}-complete - *M. Schaefer* |

| | | **On PAC learning using winnow, perceptron, and a perceptron-like algorithm** - *Rocco A. Servedio* |

| | | **Leaning to optimally schedule internet banner advertisements** - *Naoki Abe and Atsuyoshi Nakamura* |

| | | **Minimax regret under log loss for general classes of experts** - *Nicolò Cesa-Bianchi and Gábor Lugosi* |

| | | **Learning Information Extraction Rules for Semi-Structured and Free Text** - *Stephen Soderland* |

| | | **From Computational Learning Theory to Discovery Science** - *Osamu Watanabe* |

| | | **Convergence analysis of temporal-difference learning algorithms with linear function approximation** - *Vladislav Tadić* |

| | | **Experiments with noise filtering in a medical domain** - *Dragan Gamberger, Nada Lavrač and Ciril Grošelj* |

| | | **Learning specialist decision lists** - *Atsuyoshi Nakamura* |

| | | **On a generalized notion of mistake bounds** - *Sanjay Jain and Arun Sharma* |

| | | **Boosting as entropy projection** - *Jyrki Kivinen and Manfred K. Warmuth* |

| | | **Approximation via value unification** - *Paul E. Utgoff and David J. Stracuzzi* |

| | | **Tractable average-case analysis of naive Bayesian classifiers** - *Pat Langley and Stephanie Sage* |

| | | **Learning of first-order formulas and inductive logic programming** - *Hiroki Arimura and Kouichi Hirata* |

| | | **Efficient Read-Restricted Monotone CNF/DNF Dualization by Learning with Membership Queries** - *Carlos Domingo, Nina Mishra and Leonard Pitt* |

| | | **Learning fixed-dimension linear thresholds from fragmented data** - *Paul W. Goldberg* |

| | | **Microchoice bounds and self bounding learning algorithms** - *John Langford and Avrim Blum* |

| | | **Concept Learning and Feature Selection Based on Square-Error Clustering** - *Boris Mirkin* |

| | | **Model selection in unsupervised learning with applications to document clustering** - *Shivakumar Vaithyanathan and Byron Dom* |

| | | **OPT-KD: an algorithm for optimizing kd-trees** - *Douglas A. Talbert and Douglas H. Fisher* |

| | | **Guest Editors' Introduction: Machine Learning and Natural Language** - *Claire Cardie and Raymond J. Mooney* |

| | | **Distributed robotic learning: adaptive behavior acquisition for distributed autonomous swimming robot in real-world** - *Daisuke Iijima, Wenwei Yu, Hiroshi Yokoi and Yukinori Kakazu* |

| | | **On the inductive inference of recursive real-valued functions** - *Kalvis Aps\=ıtis, Setsuo Arikawa, Rusins Freivalds, Eiju Hirowatari and Carl H. Smith* |

| | | **Learning to Take Actions** - *Roni Khardon* |

| | | **On prediction of individual sequences relative to a set of experts in the presence of noise** - *Tsachy Weissmann and Neri Merhav* |

| | | **Distributed cooperative Bayesian learning strategies** - *K. Yamanishi* |

| | | **Lower Bounds on the Rate of Convergence of Nonparametric Pattern Recognition** - *András Antos* |

| | | **Associative reinforcement learning using linear probabilistic concepts** - *Naoki Abe and Philip M. Long* |

| | | **Universal Distributions and Time-Bounded Kolmogorov Complexity** - *Rainer Schuler* |

| | | **Uniform-distribution attribute noise learnability** - *Nader H. Bshouty, Jeffrey C. Jackson and Christino Tamon* |

| | | **An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants** - *Eric Bauer and Ron Kohavi* |

| | | **On the Sample Complexity for Nonoverlapping Neural Networks** - *Michael Schmitt* |

| | | **Sample-efficient strategies for learning in the presence of noise** - *Nicolò Cesa-Bianchi, Eli Dichterman, Paul Fischer, Eli Shamir and Hans Ulrich Simon* |

| | | **The Computational Limits to the Cognitive Power of the Neuroidal Tabula Rasa** - *Jiri Wiedermann* |

| | | **On theory revision with queries** - *Robert H. Sloan and György Turán* |

| | | **The alternating decision tree learning algorithm,** - *Yoav Freund and Llew Mason* |

| | | **Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones** - *Lawrence K. Saul and Michael I. Jordan* |

| | | **Margin Distribution Bounds on Generalization** - *John Shawe-Taylor and Nello Christianini* |

| | | **On some misbehaviour of back-propagation with non-normalized RBFNs and a solution** - *Attilio Giordana and Roberto Piola* |

| | | **Learning determnistic regular grammars from stochastic samples in polynomial time** - *Rafael C. Carrasco and Jose Oncina* |

| | | **Learning discriminatory and descriptive rules by an inductive logic programming system** - *Maziar Palhang and Arcot Sowmya* |

| | | **An Algorithm that Learns What's in a Name** - *Daniel M. Bikel, Richard Schwartz and Ralph M. Weischedel* |

| | | **Pasting Small Votes for Classification in Large Databases and On-Line** - *Leo Breiman* |

| | | **Learning comprehensible descriptions of multivariate time series** - *Mohammed Waleed Kadous* |

| | | **Distributed value functions** - *Jeff Schneider, Weng-Keen Wong, Andrew Moore and Martin Riedmiller* |

| | | **Inductive Learning with Corroboration** - *Phil Watson* |

| | | **An Efficient Method To Estimate Bagging's Generalization Error** - *David Wolpert and William G. Macready* |

| | | **The Consistency Dimension and Distribution-Dependent Learning from Queries** - *Jose L. Balcazar, Jorge Castro, David Guijarro and Hans-Ulrich Simon* |

| | | **A minimum risk metric for nearest neighbor classification** - *Enrico Blanzieri and Francesco Ricci* |

| | | **Toward a Model of Intelligence as an Economy of Agents** - *Eric B. Baum* |

| | | **On Error Estimation for the Partitioning Classification Rule** - *Márta Horváth:* |

| | | **Viewing all models as `probabilistic'** - *Peter Grünwald* |

| | | **Learning Minimal Covers of Functional Dependencies with Queries** - *Montserrat Hermo and Vitor Lavin* |

| | | **An adaptive version of the boost by majority algorithm** - *Yoav Freund* |

| | | **Efficient non-linear control by combining Q-learning with local linear controllers** - *Hajime Kimura and Shigenobu Kobayashi* |

| | | **Some elements of machine learning** - *J. R. Quinlan* |

| | | **Learning DNF over the Uniform Distribution Using a Quantum Example Oracle** - *Nader H. Bshouty and Jeffrey C. Jackson* |

| | | **Finding a Minimal 1-DNF Consistent with a Positive Sample is LOGSNP-Complete** - *F. Denis* |

| | | **On the Uniform Learnability of Approximations to Non-Recursive Functions** - *Frank Stephan and Thomas Zeugmann* |

| | | **Paradigms in Measure Theoretic Learning and in Informant Learning** - *Franco Montagna and Giulia Simi* |

| | | **Regret bounds for prediction problems** - *Geoffrey J. Gordon* |

| | | **Learning policies with external memory** - *Leonid Peshkin, Nicolas Meuleau and Leslie Pack Kaelbling* |

| | | **On the complexity of learning for spiking neurons with temporal coding** - *W. Maass and M. Schmitt* |

| | | **Costs of General Purpose Learning** - *John Case, Keh-Jiann Chen and Sanjay Jain* |

| | | **Machine-learning applications of algorithmic randomness** - *Volodya Vovk, Alex Gammerman and Craig Saunders* |

| | | **Correcting noisy data** - *Choh Man Teng* |

| | | **Sonar-based mapping with mobile robots using EM** - *Wolfram Burgard, Dieter Fox, Hauke Jans, Christian Matenar and Sebastian Thrun* |

| | | **Entropy Numbers, Operators and Support Vector Kernels** - *Robert C. Williamson, Alex J. Smola and Bernhard Schölkopf* |

| | | **Simple Flat Languages: A Learnable Class in the Limit from Positive Data** - *T. Okadome* |

| | | **Incremental concept learning for bounded data mining** - *J. Case, S. Jain, S. Lange and T. Zeugmann* |

| | | **A Geometric Approach to Leveraging Weak Learners** - *Nigel Duffy and David P. Helmbold* |

| | | **Careful abstraction from instance families in memory-based language learning** - *Antal Van Den Bosch* |

| | | **Learning to Coordinate; a Recursion Theoretic Perspective** - *Franco Montagna and Daniel Osherson* |

| | | **PAC-Bayesian model averaging** - *David A. McAllester* |

| | | **Maximal machine learnable classes** - *J. Case and M. A. Fulk* |

| | | **Least-squares temporal difference learning** - *Justin A. Boyan* |

| | | **Learning to Parse Natural Language with Maximum Entropy Models** - *Adwait Ratnaparkhi* |

| | | **Using Decision Trees to Construct a Practical Parser** - *Masahiko Haruno, Satoshi Shirai and Yoshifumi Ooyama* |

| | | **Integrating case-based learning and cognitive biases for machine learning of natural language** - *Claire Cardie* |

| | | **Estimation of Time-Varying Parameters in Statistical Models: An Optimization Approach** - *Dimitris Bertsimas, David Gamarnik and John N. Tsitsiklis* |

| | | **Open Theoretical Questions in Reinforcement Learning** - *Richard S. Sutton* |

| | | **Improved Boosting Algorithms Using Confidence-rated Predictions** - *Robert E. Schapire and Yoram Singer* |

| | | **Learning Real Polynomials with a Turing Machine** - *Dennis Cheung* |

| | | **Extended Stochastic Complexity and Minimax Relative Loss Analysis** - *Kenji Yamanishi* |

| | | **Exact learning when irrelevant variables abound** - *D. Guijarro, V. Lavin and V. Raghavan* |

| | | **Statistical Models for Text Segmentation** - *Doug Beeferman, Adam Berger and John D. Lafferty* |

| | | **Regularized Principal Manifolds** - *Alex J. Smola, Robert C. Williamson, Sebastian Mika and Bernhard Schölkopf* |

| | | **Robust behaviorally correct learning** - *S. Jain* |

| | | **Learnability of Enumerable Classes of Recursive Functions from "Typical" Examples** - *Jochen Nessel* |

| | | **Drifting Games** - *Robert E. Schapire* |

| | | **Induction of Logic Programs Based on ***psi*-Terms - *Yutaka Sasaki* |

| | | **Transductive inference for text classification using support vector machines** - *Thorsten Joachims* |

| | | **On Teaching and Learning Intersection-Closed Concept Classes** - *Christian Kuhlmann* |

| | | **Using reinforcement learning to spider the web efficiently** - *Jason Rennie and Andrew Kachites McCallum* |

| | | **Boosting a strong learner: evidence against the minimum margin** - *Michael Harries* |

| | | **Beating the hold-out: bounds for k-fold and progressive cross-validation** - *Avrim Blum, Adam Kalai and John Langford* |

| | | **Query by Committee, Linear Separation and Random Walks** - *Ran Bachrach, Shai Fine and Eli Shamir* |

| | | **On the boosting ability of top-down decision tree learning algorithms** - *M. Kearns and Y. Mansour* |

| | | **Learnability of Quantified Formula** - *Victor Dalmau and Peter Jeavons* |

| | | **Guest Editors' Introduction** - *Jonathan Baxter and Nicolò Cesa-Bianchi* |

| | | **GA-based learning of context-free grammars using tabular representations** - *Yasubumi Sakakibara and Mitsuhiro Kondo* |

| | | **Derandomizing Stochastic Prediction Strategies** - *V. G. Vovk* |

| | | **Detecting motifs from sequences** - *Yuh-Jyh Hu, Suzanne Sandmeyer and Dennis Kibler* |

| | | **Linearly Combining Density Estimators via Stacking** - *Padhraic Smyth and David Wolpert* |

| | | **Multiclass learning, boosting, and error-correcting codes** - *Venkatesan Guruswami and Amit Sahai* |

| | | **A Dichotomy Theorem for Learning Quantified Boolean Formulas** - *Victor Dalmau* |

| | | **An Efficient Extension to Mixture Techniques for Prediction and Decision Trees** - *Fernando C. N. Pereira and Yoram Singer* |

| | | **On the V**_{\}gamma Dimension for Regression in Reproducing Kernel Hilbert Spaces - *Theodoros Evgeniou and Massimiliano Pontil* |

| | | **The functions of finite support: a canonical learning problem** - *Rusins Freivalds, Efim Kinber and Carl H. Smith* |

| | | **Using Correspondence Analysis to Combine Classifiers** - *Christopher J. Merz* |

| | | **Exploration of Multi-State Environments: Local Measures and Back-Propagation of Uncertainty** - *Nicolas Meuleau and Paul Bourgine* |

| | | **The learnability of unions of two rectangles in the two-dimensional discretized space** - *Z. Chen and F. Ameur* |

| | | **Monte Carlo hidden Markov models: Learning non-parametric models of partially observable stochastic processes** - *Sebastian Thrun, John C. Langford and Dieter Fox* |

| | | **Active learning for natural language parsing and information extraction** - *Cynthia A. Thompson, Mary Elaine Califf and Raymond J. Mooney* |

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

| | | **Simple DFA are polynomially probably exactly learnable from simple examples** - *Rajesh Parekh and Vasant Honavar* |

| | | **Mind Change Complexity of Learning Logic Programs** - *Sanjay Jain and Arun Sharma* |

| | | **An accelerated Chow and Liu algorithm: fitting tree distributions to high-dimensional sparse data** - *Marina Meila* |

| | | **On the intrinsic complexity of learning recursive functions** - *Efim Kinber, Christophe Papazian, Carl Smith and Rolf Wiehagen* |

| | | **Large margin trees for induction and transduction** - *Donghui Wu, Kristin P. Bennett, Nello Cristianini and John Shawe-Taylor* |

| | | **Proper learning algorithm for functions of k terms under smooth distributions** - *Y. Sakai, E. Takimoto and A. Maruoka* |

| | | **Distribution-Dependent Vapnik-Chervonenkis Bounds** - *Nicolas Vayatis and Robert Azencott* |

| | | **Feature engineering for text classification** - *Sam Scott and Stan Matwin* |

| | | **Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness to the E**^{3} Algorithm - *Carlos Domingo* |

| | | **Learning to ride a bicycle using iterated phantom induction** - *Mark Brodie and Gerald DeJong* |

| | | **Machine Learning** - *Thomas G. Dietterich* |

| | | **Ordinal mind change complexity of language identification** - *Andris Ambainis, Sanjay Jain and Arun Sharma* |

| | | **Learning Bayesian Belief Networks Based on the MDL Principle: An Efficient Algorithm Using the Branch and Bound Technique** - *Joe Suzuki* |

| | | **Theoretical Views of Boosting** - *Robert E. Schapire* |

| | | **Direct and Indirect Algorithms or On-line Learning of Disjunctions** - *David P. Helmbold, Sandra Panizza and Manfred K. Warmuth* |

| | | **A Note on Support Vector Machine Degeneracy** - *Ryan Rifkin, Massimiliano Pontil and Alessandro Verri* |

| | | **Projection Learning** - *Leslie G. Valiant* |

| | | **Discriminant trees** - *João Gama* |

| | | **Further results on the margin distribution** - *John Shawe-Taylor and Nello Cristianini* |

| | | **Making better use of global discretization** - *Eibe Frank and Ian H. Witten* |

| | | **Tailoring Representations to Different Requirements** - *Katharina Morik* |

| | | **Learning to Reason with a Restricted View** - *Roni Khardon and Dan Roth* |

| | | **Effective and Efficient Knowledge Base Refinement** - *Leonardo Carbonara and Derek Sleeman* |

| | | **On a question of nearly minimal identification of functions** - *S. Jain* |

| | | **Systems that Learn: An Introduction to Learning Theory, second edition** - *Sanjay Jain, Daniel Osherson, James S. Royer and Arun Sharma* |

| | | **Identifying Mislabeled Training Data** - *C. E. Brodley and M. A. Friedl* |

| | | **Linear relations between square-loss and Kolmogorov complexity** - *Yuri Kalnishkan* |

| | | **Abstracting from robot sensor data using hidden Markov models** - *Laura Firoiu and Paul R. Cohen* |

| | | **Local learning for iterated time series prediction** - *Gianluca Bontempi, Mauro Birattari and Hugues Bersini* |

| | | **Exploring Unknown Environments** - *Susanne Albers and Monika R. Henzinger* |

| | | **Covering numbers for support vector machines** - *Ying Guo, Peter L. Bartlett, John Shawe-Taylor and Robert C. Williamson* |

| | | **Unsupervised visual learning of three-dimensional objects using a modular network architecture** - *S. Suzuki H. Ando and T. Fujita* |

| March | | **Computational Learning Theory, 4th European Conference, EuroCOLT '99, Nordkirchen, Germany, March 29-31, 1999, Proceedings** - *Paul Fischer and Hans-Ulrich Simon* |

| July | | **Learning Classes of Approximations to Non-Recursive Functions** - *F. Stephan and T. Zeugmann* |

| December | | **Algorithmic Learning Theory, 10th International Conference, ALT '99, Tokyo, Japan, December 1999, Proceedings** - *Osamu Watanabe and Takashi Yokomori* |