| 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 Sigmap3-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 E3 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 |
