2001 | | | **Aspects of complexity of probabilistic learning under monotonicity constraints** - *Léa Meyer* |

| | | **Inventing Discovery Tools: Combining Information Visualization with Data Mining** - *Ben Shneiderman* |

| | | **Expectation Maximization for Weakly Labeled Data** - *Yuri Ivanov, Bruce Blumberg and Alex Pentland* |

| | | **Induction of Qualitative Trees** - *Dorian Suc and Ivan Bratko* |

| | | **Introduction** - *Vasant Honavar and Colin de la Higuera* |

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

| | | **Monotone term decision lists** - *David Guijarro, Victor Lavin and Vijay Raghavan* |

| | | **A Computational Model for Children's Language Acquisition Using Inductive Logic Programming** - *Ikuo Kobayashi, Koichi Furukawa, Tomonobu Ozaki and Mutsumi Imai* |

| | | **Convergence of Gradient Dynamics with a Variable Learning Rate** - *Michael Bowling and Manuela Veloso* |

| | | **Finding Best Patterns Practically** - *Ayumi Shinohara, Masayuki Takeda, Setsuo Arikawa, Masahiro Hirao, Hiromasa Hoshino and Shunsuke Inenaga* |

| | | **When Can Two Unsupervised Learners Achieve PAC Separation?** - *Paul W. Goldberg* |

| | | **Non-linear Inequalities between Predictive and Kolmogorov Complexities** - *Michael V. Vyugin and Vladimir V. V'yugin* |

| | | **Coupled Clustering: a Method for Detecting Structural Correspondence** - *Zvika Marx, Ido Dagan and Joachim Buhmann* |

| | | **Inductive Thermodynamics from Time Series Data Analysis** - *Hiroshi H. Hasegawa, Takashi Washio and Yukari Ishimiya* |

| | | **Visual Development and the Acquisition of Binocular Disparity Sensitivities** - *Melissa Dominguez and Robert A. Jacobs* |

| | | **Refutable Language Learning with a Neighbor System** - *Yasuhito Mukouchi and Masako Sato* |

| | | **Support Vectors for Reinforcement Learning** - *Thomas G. Dietterich and Xin Wang* |

| | | **Efficient Data Mining by Active Learning** - *Hiroshi Mamitsuka and Naoki Abe* |

| | | **Optimizing Epochal Evolutionary Search: Population-Size Dependent Theory** - *Erik Van Nimwegen and James P. Crutchfield* |

| | | **Discrete Prediction Games with Arbitrary Feedback and Loss** - *Antonio Piccolboni and Christian Schindelhauer* |

| | | **An Efficient Approach for Approximating Multi-Dimensional Range Queries and Nearest Neighbor Classification in Large Datasets** - *Carlotta Domeniconi and Dimitrios Gunopulos* |

| | | **A Multi-Agent, Policy-Gradient Approach to Network Routing** - *Nigel Tao, Jonathan Baxter and Lex Weaver* |

| | | **Composite Kernels for Hypertext Categorisation** - *Thorsten Joachims, Nello Cristianini and John Shawe-Taylor* |

| | | **Queries Revisited** - *Dana Angluin* |

| | | **On the Use of Pairwise Comparison of Hypotheses in Evolutionary Learning Applied to Learning from Visual Examples** - *Krzysztof Krawiec* |

| | | **Hierarchies of probabilistic and team FIN-learning** - *Andris Ambainis, Kalvis Aps\=ıtis, Rīsiņs Freivalds and Carl H. Smith* |

| | | **Tempral Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications** - *Rainer Schmidt and Lothar Gierl* |

| | | **Using EM to Learn 3D Models of Indoor Environments with Mobile Robots** - *Yufeng Lui, Rosemary Emery, Deepayan Charabarti, Wolfram Burgard and Sebastian Thrun* |

| | | **Second Difference Method Reinforced by Grouping: A New Tool for Assistance in Assignment of Complex Molecular Spectra** - *Takehiko Tanaka* |

| | | **A comparison of identification criteria for inductive inference of recursive real-valued functions** - *Eiju Hirowatari and Setsuo Arikawa* |

| | | **Searching for Mutual Exclusion Algorithms Using BDDs** - *Koichi Takahashi and Masami Hagiya* |

| | | **Predicting the Future of Discrete Sequences from Fractal Representations of the Past** - *Peter Tino and Georg Dorffner* |

| | | **A Hybrid Tool for Data Mining in Picture Archiving System** - *Petra Perner and Tatjana Belikova* |

| | | **Latent Semantic Kernels** - *Nello Cristianini, John Shawe-Taylor and Huma Lodhi* |

| | | **Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems** - *Makoto Sato and Shigenobu Kobayashi* |

| | | **Algorithmic Aspects of Boosting** - *Osamu Watanabe* |

| | | **Relational Learning with Statistical Predicate Invention: Better Models for Hypertext** - *Mark Craven and Seán Slattery* |

| | | **A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Hold'em Poker** - *Fredrik A. Dahl* |

| | | **Scaling Reinforcement Learning toward RoboCup Soccer** - *Peter Stone and Richard S. Sutton* |

| | | **Learning XML Grammars** - *Henning Fernau* |

| | | **On Using Extended Statistical Queries to Avoid Membership Queries** - *Nader H. Bshouty and Vitaly Feldman* |

| | | **Clustering Continuous Time Series** - *Paola Sebastiani and Marco Ramoni* |

| | | **Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density** - *Amy McGovern and Andrew G. Barto* |

| | | **Infinite-Horizon Policy-Gradient Estimation** - *J. Baxter and P. L. Bartlett* |

| | | **Complexity of learning in artificial neural networks** - *Andreas Engel* |

| | | **Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems** - *Tobias Scheffer and Stefan Wrobel* |

| | | **PAC Learning under Helpful Distributions** - *François Denis and Rémi Gilleron* |

| | | **A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems** - *David J. Hand and Robert J. Till* |

| | | **Nonlinear Function Learning and Classification Using Optimal Radial Basis Function Networks** - *Adam Krzyżak* |

| | | **Robust Learning - Rich and Poor** - *John Case, Sanjay Jain, Frank Stephan and Rolf Wiehagen* |

| | | **Face Detection by Aggregated Bayesian Network Classifiers** - *Thang V. Pham, Marcel Worring and Arnold W. M. Smeulders* |

| | | **A Guided Tour of Finite Mixture Models: from Pearson to the Web** - *Padhraic Smyth* |

| | | **Concept Decompositions for Large Sparse Text Data Using Clustering** - *Inderjit S. Dhillon and Dharmendra S. Modha* |

| | | **Evolutionary Search, Stochastic Policies with Memory, and Reinforcement Learning with Hidden State** - *Matthew R. Glickman and Katia Sycara* |

| | | **Discovering Communicable Scientific Knowledge from Spatio-Temporal Data** - *Mark Schwabacher and Pat Langley* |

| | | **Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference** - *C. Lee Giles, Steve Lawrence and Ah Chung Tsoi* |

| | | **Foreword** - *Ming Li* |

| | | **Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner** - *Kurt Driessens, Jan Ramon and Hendrik Blockeel* |

| | | **A Mixture Approach to Novelty Detection Using Training Data with Outliers** - *Martin Lauer* |

| | | **Feature Construction with Version Spaces for Biochemical Applications** - *Stefan Kramer and Luc De Raedt* |

| | | **Learning Probabilistic Models of Relational Structure** - *Lise Getoor, Nir Friedman, Daphne Koller and Benjamin Taskar* |

| | | **A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal-Difference Learning** - *David Choi and Benjamin Van Roy* |

| | | **Validation of Text Clustering Based on Document Contents** - *Jarmo Toivonen, Ari Visa, Tomi Vesanen, Barbro Back and Hannu Vanharanta* |

| | | **Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection** - *Sanmay Das* |

| | | **Statistical and Neural Approaches for Estimating Parameters of a Speckle Model Based on the Nakagami Distribution** - *Mark P. Wachowiak, Renata Smol\'ıková, Mariofanna G. Milanova and Adel S. Elmaghraby* |

| | | **Worst-Case Bounds for the Logarithmic Loss of Predictors** - *Nicolò Cesa-Bianchi and Gábor Lugosi* |

| | | **Featureless Pattern Recognition in an Imaginary Hilbert Space and Its Applicaton to Protein Fold Classification** - *Vadim Mottl, Sergey Dvoenko, Oleg Seredin, Casimir Kulikowski and Ilya Muchnik* |

| | | **Technology of Text Mining** - *Ari Visa* |

| | | **Convergence and Error Bounds for Universal Prediction of Nonbinary Sequences** - *Marcus Hutter* |

| | | **Rule-Based Ensemble Solutions for Regression** - *Nitin Indurkhya and Sholom M. Weiss* |

| | | **Finding of Signal and Image by Integer-Type Haar Lifting Wavelet Transform** - *Koichi Niijima and Shigeru Takano* |

| | | **Agnostic Learning of Geometric Patterns** - *Sally A. Goldman, Stephen S. Kwek and Stephen D. Scott* |

| | | **Classification on Data with Biased Class Distribution** - *Slobodan Vucetic and Zoran Obradovic* |

| | | **Geometric Methods in the Analysis of Glivenko-Cantelli Classes** - *Shahar Mendelson* |

| | | **Discovery of Positive and Negative Knowledge in Medical Databases Using Rough Sets** - *Shusaku Tsumoto* |

| | | **Visualization and Analysis of Web Graphs** - *Sachio Hirokawa and Daisuke Ikeda* |

| | | **Discovering Mechanisms: A Computational Philosophy of Science Perspective** - *Lindley Darden* |

| | | **Learning to Generate Fast Signal Processing Implementations** - *Bryan Singer and Manuela Veloso* |

| | | **Theory of Judgments and Derivations** - *Masahiko Sato* |

| | | **Predicting nearly as well as the best pruning of a decision tree through dynamic programming scheme** - *Eiji Takimoto, Akira Maruoka and Volodya Vovk* |

| | | **Machine Learning and Data Mining in Pattern Recognition, Second International Workshop, MLDM 2001, Leipzig, Germany, July 2001, Proceedings** - *Petra Perner* |

| | | **Application of Multivariate Maxwellian Mixture Model to Plasma Velocity Distribution** - *Genta Ueno, Nagatomo Nakamura, Tomoyuki Higuchi, Takashi Tsuchiya, Shinobu Machida and Tohru Araki* |

| | | **Learning Languages in a Union** - *Sanjay Jain, Yen Kaow Ng and Tiong Seng Tay* |

| | | **On the Synthesis of Strategies Identifying Recursive Functions** - *Sandra Zilles* |

| | | **An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning** - *Wojciech Kwedlo and Marek Kretowski* |

| | | **On Agnostic Learning with {0,***ast*,1}-Valued and Real-Valued Hypotheses - *Philip M. Long* |

| | | **Learning When to Collaborate among Learning Agents** - *antiago Ontañón Enric Plaza* |

| | | **Some Theoretical Aspects of Boosting in the Presence of Noisy Data** - *Wenxin Jiang* |

| | | **Building Committees by Clustering Models Based on Pairwise Similarity Values** - *Thomas Ragg* |

| | | **On No-Regret Learning, Fictitious Play, and Nash Equilibrium** - *Amir Jafari, Amy Greenwald, David Gondek and Gunes Ercal* |

| | | **Refutable/Inductive Learning from Neighbor Examples and Its Application to Decision Trees over Patterns** - *Masako Sato, Yasuhito Mukouchi and Mikiharu Terada* |

| | | **A Flexible Modeling of Global Plasma Profile Deduced from Wave Data** - *Yoshitaka Goto, Yoshiya Kasahara and Toru Sato* |

| | | **Application of Fuzzy Similarity-Based Fractal Dimensions to Characterize Medical Time Series** - *Manish Sarkar and Tze-Yun Leong* |

| | | **FAM-Based Fuzzy Interence for Detecting Shot Transitions** - *Seok-Woo Jang, Gyo-young Kim and Hyung-Il Choi* |

| | | **Language Simplification through Error-Correcting and Grammatical Inference Techniques** - *Juan-Carlos Amengual, Alberto Sanchis, Enrique Vidal and José-Miguel Benedi* |

| | | **A procedure for unsupervised lexicon learning** - *Anand Venkataraman* |

| | | **On the role of search for learning from examples** - *Stuart A. Kurtz, Carl H. Smith and Rolf Wiehagen* |

| | | **Constructing Inductive Applications by Meta-Learning with Method Repositories** - *Hidenao Abe and Takahira Yamaguchi* |

| | | **Knowledge Discovery in Auto-tuning Parallel Numerical Library** - *Hisayasu Kuroda, Takahiro Katagiri and Yasumasa Kanada* |

| | | **Accelerating EM for Large Databases** - *Bo Thiesson, Christopher Meek and David Heckerman* |

| | | **Bias Correction in Classification Tree Construction** - *Alin Dobra and Johannes Gehrke* |

| | | **Structured Prioritized Sweeping** - *Richard Dearden* |

| | | **Machine Learning: ECML 2001, 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001, Proceedings** - *Luc De Raedt and Peter Flach* |

| | | **Computational Analysis of Plasma Waves and Particles in the Auroral Region Observed by Scientific Satellite** - *Yoshiya Kasahara, Ryotaro Niitsu and Toru Sato* |

| | | **Mixtures of Rectangles: Interpretable Soft Clustering** - *Dan Pelleg and Andrew Moore* |

| | | **Geometric Properties of Naive Bayes in Nominal Domains** - *Huajie Zhang and Charles X. Ling* |

| | | **Using Iterated Bagging to Debias Regressions** - *Leo Breiman* |

| | | **Discovering Admissible Simultaneous Equation Models from Observed Data** - *Takashi Washio, Hiroshi Motoda and Yuji Niwa* |

| | | **Some Independence Results for Control Structures in Complete Numberings** - *Sanjay Jain and Jochen Nessel* |

| | | **Direct Policy Search Using Paired Statistical Tests** - *Malcolm J. A. Strens and Andrew W. Moore* |

| | | **On the VC Dimension of Bounded Margin Classifiers** - *Don Hush and Clint Scovel* |

| | | **Importance Sampling Techniques in Neural Detector Training** - *José Sanz-González and Diego Andina* |

| | | **Learning Additive Models Online with Fast Evaluating Kernels** - *Mark Herbster* |

| | | **Learning Recursive Functions Refutably** - *Sanjay Jain, Efim Kinber, Rolf Wiehagen and Thomas Zeugmann* |

| | | **Off-Policy Temporal-Difference Learning with Function Approximation** - *Doina Precup, Richard S. Sutton and Sanjoy Dasgupta* |

| | | **Automatic Indentification of Diatoms Using Decision Forests** - *Stefan Fischer and Horst Bunke* |

| | | **Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example** - *Günther Eibl and Karl Peter Pfeiffer* |

| | | **Limitations of Learning Via Embeddings in Euclidean Half-Spaces** - *Shai Ben-David, Nadav Eiron and Hans Ulrich Simon* |

| | | **Adaptive Query Shifting for Content-Based Image Retrieval** - *Giorgio Giacinto, Fabio Roli and Giorgio Fumera* |

| | | **Agnostic Boosting** - *Shai Ben-David, Philip M. Long and Yishay Mansour* |

| | | **An Adaptive Version of the Boost by Majority Algorithm** - *Yoav Freund* |

| | | **Data Compression Method Combining Properties of PPM and CTW** - *Takumi Okazaki, Kunihiko Sadakane and Hiroshi Imai* |

| | | **Inducing Partially-Defined Instances with Evolutionary Algorithms** - *Xavier Llorà and Josep M. Garrell* |

| | | **An Experimental Comparison of Model-Based Clustering Methods** - *Marina Meila and David Heckerman* |

| | | **Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining** - *Heikki Mannila* |

| | | **Learning with Maximum-Entropy Distributions** - *Yishay Mansour and Mariano Schain* |

| | | **Evaluation of Clinical Relevance of Clinical Laboratory Investigations by Data Mining** - *Ulrich Sack and Manja Kamprad* |

| | | **Robust Learning with Missing Data** - *Marco Ramoni and Paola Sebastiani* |

| | | **Cryptographic limitations on parallelizing membership and equivalence queries with applications to random-self-reductions** - *Marc Fischlin* |

| | | **Soft Margins for AdaBoost** - *G. Rätsch, T. Onoda and K.-R. Müller* |

| | | **Evolutionary Trees Can be Learned in Polynomial Time in the Two-State General Markov Model** - *Mary Cryan, Leslie Ann Goldberg and Paul W. Goldberg* |

| | | **General Convergence Results for Linear Discriminant Updates** - *Adam J. Grove, Nick Littlestone and Dale Schuurmans* |

| | | **A Theory-Refinement Approach to Information Extraction** - *Tina Eliassi-Rad and Jude Shavlik* |

| | | **The Effect of Relational Background Knowledge on Learning of Protein Three-Dimensional Fold Signatures** - *Marcel Turcotte, Stephen H. Muggleton and Michael J. E. Sternberg* |

| | | **Learning How to Separate** - *Sanjay Jain and Frank Stephan* |

| | | **WBC**_{SVM}: Weighted Bayesian Classification Based on Support Vector Machines - *Thomas Gärtner and Peter A. Flach* |

| | | **A note on a scale-sensitive dimension of linear bounded functionals in Banach spaces** - *Leonid Gurvits* |

| | | **Meme Media for Re-editing and Redistributing Intellectual Assets and Their Application to Interactive Virtual Information Materialization** - *Yuzuru Tanaka* |

| | | **Collaborative Learning for Recommender Systems** - *Wee Sun Lee* |

| | | **On Learning Correlated Boolean Functions Using Statistical Queries (Extended Abstract)** - *Ke Yang* |

| | | **Learning Monotone DNF From a Teacher That Almost Does Not Answer Membership Queries** - *Nader H. Bshouty and Nadav Eiron* |

| | | **Efficient algorithms for decision tree cross-validation** - *Hendrik Blockeel and Jan Struyf* |

| | | **Rademacher and Gaussian Complexities: Risk Bounds and Structural Results** - *Peter L. Bartlett and Shahar Mendelson* |

| | | **Costs of general purpose learning** - *John Case, Keh-Jiann Chen and Sanjay Jain* |

| | | **Efficient Learning of Semi-structured Data from Queries** - *Hiroki Arimura, Hiroshi Sakamoto and Setsuo Arikawa* |

| | | **Improving Probabilistic Grammatical Inference Core Algorithms with Post-Processing Techniques** - *Franck Thollard* |

| | | **Using Subclasses to Improve Classification Learning** - *Achim Hoffmann, Rex Kwok and Paul Compton* |

| | | **Learning structure from sequences, with applications in a digital library** - *Ian H. Witten* |

| | | **A Theoretical Analysis of Query Selection for Collaborative Filtering** - *Wee Sun Lee and Philip M. Long* |

| | | **Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery** - *Ljupco Todorovski and Saso Dzeroski* |

| | | **Polynomial Learnability of Stochastic Rules with Respect to the KL-Divergence and Quadratic Distance** - *Naoki Abe, Jun-ichi Takeuchi and Manfred K. Warmuth* |

| | | **Scalable and Comprehensible Visualization for Discovery of Knowledge from the Internet** - *Etsuya Shibayama, Masashi Toyoda, Jun Yabe and Shin Takahashi* |

| | | **Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation** - *Gregory Shakhnarovich, Ran El-Yaniv and Yoram Baram* |

| | | **General Loss Bounds for Universal Sequence Prediction** - *Marcus Hutter* |

| | | **Improving Term Extraction by System Combination Using Boosting** - *Jordi Vivaldi, Llu\'ıs Màrquez and Horacio Rodr\'ıguez* |

| | | **Data Mining Approach Based on Information-Statistical Analysis: Application to Temporal-Spatial Data** - *Bon K. Sy and Arjun K. Gupta* |

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

| | | **Mirror Image Learning for Handwritten Numeral Recognition** - *Meng Shi, Tetsushi Wakabayashi, Wataru Ohyama and Fumitaka Kimura* |

| | | **Towards Self-Exploring Discriminating Features** - *Ying Wu and Thomas S. Huang* |

| | | **Constructing a Critical Casebase to Represent a Lattice-Based Relation** - *Ken Satoh* |

| | | **Learning Coherent Concepts** - *Ashutosh Garg and Dan Roth* |

| | | **In Pursuit of Interesting Patterns with Undirected Discovery of Exception Rules** - *Einoshin Suzuki* |

| | | **New Error Bounds for Solomonoff Prediction** - *Marcus Hutter* |

| | | **Local Learning Framework for Recognition of Lowercase Handwritten Characters** - *Jian-xiong Dong, Adam Krzyżak and C. Y. Suen* |

| | | **Robust learning with infinite additional information** - *Susanne Kaufmann and Frank Stephan* |

| | | **Multiple Instance Regression** - *Soumya Ray and David Page* |

| | | **Language Learning from Texts: Degrees of Intrinsic Complexity and Their Characterizations** - *Sanjay Jain, Efim Kinber and Rolf Wiehagen* |

| | | **Learning Regular Sets with an Incomplete Membership Oracle** - *Nader H. Bshouty and Avi Owshanko* |

| | | **Robot Baby 2001** - *Paul R. Cohen, Tim Oates, Niall Adams and Carole R. Beal* |

| | | **Efficient Algorithms for the Inference of Minimum Size DFAs** - *Arlindo L. Oliveira and João P. M. Silva* |

| | | **Classification of Object Sequences Using Syntactical Structure** - *Atsuhiro Takasu* |

| | | **Discovering Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables** - *Ryohei Nakano and Kazumi Saito* |

| | | **Optimizing Average Reward Using Discounted Rewards** - *Sham Kakade* |

| | | **Quantum Neural Networks** - *Sanjay Gupta and R. K. P. Zia* |

| | | **Supervised Versus Unsupervised Binary-Learning by Feedforward Neural Networks** - *Nathalie Japkowicz* |

| | | **Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers** - *Bianca Zadrozny and Charles Elkan* |

| | | **Learning Embedded Maps of Markov Processes** - *Yaakov Engel and Shie Mannor* |

| | | **On Boosting with Optimal Poly-Bounded Distributions** - *Nader H. Bshouty and Dmitry Gavinsky* |

| | | **Further Explanation of the Effectiveness of Voting Methods: The Game Between Margins and Weights** - *Vladimir Koltchinskii, Dmitriy Panchenko and Fernando Lozano* |

| | | **Potential-Based Algorithms in On-line Prediction and Game Theory** - *Nicolò Cesa-Bianchi and Gabor Lugosi* |

| | | **Are Case-Based Reasoning and Dissimilarity-Based Classification Two Sides of the Same Coin?** - *Petra Perner* |

| | | **Second Order Features for Maximising Text Classification Performance** - *Bhavani Raskutti, Herman Ferrá and Adam Kowalczyk* |

| | | **Multiple-Instance Learning of Real-Valued Data** - *Robert A. Amar, Daniel R. Dooly, Sally A. Goldman and Qi Zhang* |

| | | **DQL: A New Updating Strategy for Reinforcement Learning Based on Q-Learning** - *Carlos E. Mariano and Eduardo F. Morales* |

| | | **Toward a Computational Theory of Data Acquisition and Truthing** - *David G. Stork* |

| | | **Stochastic Inference of Regular Tree Languages** - *Rafael C. Carrasco, Jose Oncina and Jorge Calera-Rubio* |

| | | **Learning Expressions over Monoids** - *Ricard Gavaldà and Denis Thérien* |

| | | **Boosting Noisy Data** - *Abba Krieger, Chuan Long and Abraham Wyner* |

| | | **WWW Visualization Tools for Discovering Interesting Web Pages** - *Hironori Hiraishi and Fumio Mizoguchi* |

| | | **Learning algebraic structures from text** - *Frank Stephan and Yuri Ventsov* |

| | | **Lazy Induction of Descriptions for Relational Case-Based Learning** - *Eva Armengol and Enric Plaza* |

| | | **Efficiently Determining the Starting Sample Size for Progressive Sampling** - *Baohua Gu, Bing Liu, Feifang Hu and Huan Liu* |

| | | **Successes, Failures, and New Directions in Natural Language Learning** - *Claire Cardie* |

| | | **Learning Rates for Q-Learning** - *Eyal Even-Dar and Yishay Mansour* |

| | | **Toward Optimal Active Learning through Sampling Estimation of Error Reduction** - *Nicholas Roy and Andrew McCallum* |

| | | **Ideal Concepts, Intuitions, and Mathematical Knowledge Acquisitions in Husserl and Hilbert** - *Mitsuhiro Okada* |

| | | **Relational Reinforcement Learning** - *Saso Dzeroski, Luc De Raedt and Kurt Driessens* |

| | | **Estimating a Kernel Fisher Discriminant in the Presence of Label Noise** - *Neil D. Lawrence and Bernhard Schölkopf* |

| | | **Constrained K-means Clustering with Background Knowledge** - *Kiri Wagstaff, Claire Cardie, Seth Rogers and Stefan Schroedl* |

| | | **Extending Elementary Formal Systems** - *Steffen Lange, Gunter Grieser and Klaus P. Jantke* |

| | | **Minimizing the Quadratic Training Error of a Sigmoid Neuron Is Hard** - *Jir\'ı ť\'ıma* |

| | | **The Evaluation of Predictive Learners: Some Theoretical and Empirical Results** - *Kevin B. Korb, Lucas R. Hope and Michelle J. Hughes* |

| | | **Comparing the Bayes and Typicalness Frameworks** - *Thomas Melluish, Craig Saunders, Ilia Nouretdinov and Volodya Vovk* |

| | | **Towards the Integration of Inductive and Nonmonotonic Logic Programming** - *Chiaki Sakama* |

| | | **On Exact Learning of Unordered Tree Patterns** - *Thomas R. Amoth, Paul Cull and Prasad Tadepalli* |

| | | **Bayesian approaches to failure prediction for disk drives** - *Greg Hamerly and Charles Elkan* |

| | | **On the learnability of recursively enumerable languages from good examples** - *Sanjay Jain, Steffen Lange and Jochen Nessel* |

| | | **A Learning Generalization Bound with an Application to Sparse-Representation Classifiers** - *Yoram Gat* |

| | | **Robot localization in a grid** - *Chinda Wongngamnit and Dana Angluin* |

| | | **Automatic Detection of Geomagnetic Jerks by Applying a Statistical Time Series Model to Geomagnetic Monthly Means** - *Hiromichi Nagao, Tomoyuki Higuchi, Toshihiko Iyemori and Tohru Araki* |

| | | **Discovering Knowledge from Graph Structured Data by Using Refutably Inductive Inference of Formal Graph Systems** - *Tetsuhiro Miyahara, Tomoyuki Uchida, Takayoshi Shoudai Tetsuji Kuboyama, Kenichi Takahashi and Hiroaki Ueda* |

| | | **Boosting Neighborhood-Based Classifiers** - *Marc Sebban, Richard Nock and Stéphane Lallich* |

| | | **Statistics of Flow Vectors and Its Application to the Voting Method for the Detection of Flow Fields** - *Atsushi Imiya and Keisuke Iwawaki* |

| | | **A Leave-One-Out Cross Validation Bound for Kernel Methods with Applications in Learning** - *Tong Zhang* |

| | | **Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions** - *Marcus Hutter* |

| | | **Learning by Switching Type of Information** - *Sanjay Jain and Frank Stephan* |

| | | **Foreword** - *Rolf Wiehagen and Thomas Zeugmann* |

| | | **Editors' Introduction** - *Naoki Abe, Roni Khardon and Thomas Zeugmann* |

| | | **Using the Genetic Algorithm to Reduce the Size of a Nearest-Neighbor Classifier and to Select Relevant Attributes** - *Antonin Rozsypal and Miroslav Kubat* |

| | | **Round Robin Rule Learning** - *Johannes Fürnkranz* |

| | | **On the Convergence of Temporal-Difference Learning with Linear Function Approximation** - *Vladislav Tadic* |

| | | **The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery** - *Gerhard Widmer* |

| | | **Learning Regular Languages from Simple Positive Examples** - *François Denis* |

| | | **Closedness properties in ex-identification** - *Kalvis Aps\=ıtis, Rīsiņs Freivalds, Raimonds Simanovskis and Juris Smotrovs* |

| | | **Learning What People (Don't) Want** - *Thomas Hofmann* |

| | | **Polynomial-time learnability of logic programs with local variables from entailment** - *M. R. K. Krishna Rao and A. Sattar* |

| | | **Unsupervised Sequence Segentation by a Mixture of Switching Variable Memory Markov Sources** - *Yevgeni Seldin, Gill Bejerano and Naftali Tishby* |

| | | **Exploration control in Reinforcement Learning Using Optimistic Model Selection** - *Jeremy L. Wyatt* |

| | | **Almost all monotone Boolean functions are polynomially learnable using membership queries** - *lya Shmulevich, Aleksey D. Korshunov and Jaakko Astola* |

| | | **Learning Regular Languages Using RFSA** - *François Denis, Aurélien Lemay and Alain Terlutte* |

| | | **Relative Loss Bounds for Multidimensional Regression Problems** - *J. Kivinen and M. K. Warmuth* |

| | | **Ridge Regressioon Confidence Machine** - *Ilia Nouretdinov, Tom Melluish and Volodya Vovk* |

| | | **Loss Functions, Complexities, and the Legendre Transformation** - *Yuri Kalnishkan, Michael V. Vyugin and Volodya Vovk* |

| | | **Applying the Bayesian Evidence Framework to ***nu*-Support Vector Regression - *Martin H. Law and James T. Kwok* |

| | | **On Dimension Reduction Mappings for Approximate Retrieval of Multi-dimensional Data** - *Takeshi Shinohara and Hiroki Ishizaka* |

| | | **14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, The Netherlands, July 2001, Proceedings** - *David Helmbold and Bob Williamson* |

| | | **Efficiently Approximating Weighted Sums with Exponentially Many Terms** - *Deepak Chawla, Lin Li and Stephen Scott* |

| | | **First-Order Rule Induction for the Recognition of Morphological Patterns in Topographic Maps** - *D. Malerba, F. Esposito, A. Lanza and F. A. Lisi* |

| | | **The Effects of Differnet Feature Sets on the Web Page Categorization Problem Using the Iterative Cross-Training Algorithm** - *Nuanwan Soonthornphisaj and Boonserm Kijsirikul* |

| | | **On the Comparison of Inductive Inference Criteria for Uniform Learning of Finite Classes** - *Sandra Zilles* |

| | | **Inference of ***omega*-Languages from Prefixes - *Colin de la Higuera and Jean-Christophe Janodet* |

| | | **Guest Editors' Introduction** - *Yoram Singer* |

| | | **Efficient Data Mining from Large Text Databases** - *Hiroki Arimura, Hiroshi Sakamoto and Setsuo Arikawa* |

| | | **Content-Based Similarity Assessment in Multi-segmented Medical Image Data Bases** - *George Potamias* |

| | | **Text Categorization Using Transductive Boosting** - *Hirotoshi Taira and Masahiko Haruno* |

| | | **Learning While Exploring: Bridging the Gaps in the Eligibility Traces** - *Fredrik A. Dahl and Ole Martin Halck* |

| | | **A Framework for Learning Rules from Multiple Instance Data** - *Yann Chevaleyre and Jean-Daniel Zucker* |

| | | **Wrapping Web Information Providers by Transducer Induction** - *Boris Chidlovskii* |

| | | **A Machine Learning Algorithm for Analyzing String Patterns Helps to Discover Simple and Interpretable Business Rules from Purchase History** - *Yukinobu Hamuro, Hideki Kawata, Naoki Katoh and Katsutoshi Yada* |

| | | **Estimating the Predictive Accuracy of a Classifier** - *Hilan Bensusan and Alexandros Kalousis* |

| | | **Robust Classification for Imprecise Environments** - *Foster Provost and Tom Fawcett* |

| | | **Intrinsic Complexity of Learning Geometrical Concepts from Positive Data** - *Sanjay Jain and Efim Kinber* |

| | | **Learning from Labeled and Unlabeled Data Using Graph Mincuts** - *Avrim Blum and Shuchi Chawla* |

| | | **Iterative Double Clustering for Unsupervised and Semi-supervised Learning** - *Ran El-Yaniv and Oren Souroujon* |

| | | **Convergence Rates of the Voting Gibbs Classifier, with Application to Bayesian Feature Selection** - *Andrew Y. Ng and Michael I. Jordan* |

| | | **Learning of Variability for Invariant Statistical Pattern Recognition** - *Daniel Keysers, Wolfgang Macherey, Jörg Dahmen and Hermann Ney* |

| | | **Linear Concepts and Hidden Variables** - *Adam J. Grove and Dan Roth* |

| | | **Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classifiction Accuracy: Evidence from a Multi-Class Problem in Remote Sensing** - *Patrice Latinne, Marco Saerens and Christine Decaestecker* |

| | | **Reducing Search Space in Solving Higher-Order Equations** - *Tetsuo Ida, Mircea Marin and Taro Suzuki* |

| | | **Random Forests** - *Leo Breiman* |

| | | **Hypertext Categorization using Hyperlink Patterns and Meta Data** - *Rayid Ghani, Seán Slattery and Yiming Yang* |

| | | **Stochastic Finite Learning** - *Thomas Zeugmann* |

| | | **Predictive learning models for concept drift** - *John Case, Sanjay Jain, Susanne Kaufmann, Arun Sharma and Frank Stephan* |

| | | **The Discovery Science Project in Japan** - *Setsuo Arikawa* |

| | | **Concepts Learning with Fuzzy Clustering and Relevance Feedback** - *Bir Bhanu and Anlei Dong* |

| | | **Packet Analysis in Congested Networks** - *Masaki Fukushima and Shigeki Goto* |

| | | **Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error** - *Gabriele Zenobi and Pádraig Cunningham* |

| | | **Toward the Discovery of First Principle Based Scientific Law Equations** - *Takashi Washio and Hiroshi Motoda* |

| | | **Some Greed Algorithms for Sparce Nonlinear Regression** - *Prasanth B. Nair, Arindam Choudhury and Andy J. Keane* |

| | | **Estimating the Optimal Margins of Embeddings in Euclidean Half Spaces** - *Jürgen Forster, Niels Schmitt and Hans Ulrich Simon* |

| | | **An Axiomatic Approach to Feature Term Generalization** - *Hassan A\"ıt-Kaci and Yutaka Sasaki* |

| | | **The Effect of Instance-Space Partition on Significance** - *Jeffrey P. Bradford and Carla E. Brodley* |

| | | **LC: A conceptual Clustering Algorithm** - *José Fco. Mart\'ınez-Trinidad and Guillermo Sanches-D'ıaz* |

| | | **Extraction of Recurrent Patterns from Stratified Ordered Trees** - *Jean-Gabriel Ganascia* |

| | | **A Unified Loss Function in Bayesian Framework for Support Vector Regression** - *Wei Chu, S. Sathiya Keerthi and Chong Jin Ong* |

| | | **Extended Association Algorithm Based on ROC Analysis for Visual Information Navigator** - *Hiroyuki Kawano and Minoru Kawahara* |

| | | **A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applications in Learning** - *Tong Zhang* |

| | | **Refuting Learning Revisited** - *Wolfgang Merkle and Frank Stephan* |

| | | **Probability theory for the Brier game** - *V. Vovk* |

| | | **Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL** - *Peter D. Turney* |

| | | **Real-Valued Multiple-Instance Learning with Queries** - *Daniel R. Dooly, Sally A. Goldman and Stephen S. Kwek* |

| | | **Strong Entropy Concentration, Game Theory and Algorithmic Randomness** - *Peter Grünwald* |

| | | **EM Learning for Symbolic-Statistical Models in Statistical Abduction** - *Taisuke Sato* |

| | | **On Learning Monotone DNF under Product Distributions** - *Rocco A. Servedio* |

| | | **How Many Queries are Needed to learn One Bit of Information?** - *Hans-Ulrich Simon* |

| | | **Bounds on Sample Size for Policy Evaluation in Markov Environments** - *Leonid Peshkin and Sayan Mukherjee* |

| | | **Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions** - *Katy S. Azoury and M. K. Warmuth* |

| | | **Geometric Bounds for Generalization in Boosting** - *Shie Mannor and Ron Meir* |

| | | **Extracting Context-Sensitive Models in Inductive Logic Programming** - *Ashwin Srinivasan* |

| | | **Boosting with Confidence Information** - *Craig W. Codrington* |

| | | **PCA-Based Model Selection and Fitting for Linear Manifolds** - *Atsushi Imiya and Hisashi Ootani* |

| | | **Relational Instance-Based Learning with Lists and Terms** - *Tamás Horváth, Stefan Wrobel and Uta Bohnebeck* |

| | | **On the limits of efficient teachability** - *Rocco A. Servedio* |

| | | **Learning DFA from Simple Examples** - *Rajesh Parekh and Vasant Honavar* |

| | | **Unsupervised Learning of Word Segmentation Rules with Genetic Algorithms and Inductive Logic Programming** - *Dimitar Kazakov and Suresh Manandhar* |

| | | **A Simpler Analysis of the Multi-way Branching Decision Tree Boosting Algorithm** - *Kohei Hatano* |

| | | **Foundations of Designing Computational Knowledge Discovery Processes** - *Yoshinori Tamada, Hideo Bannai, Osamu Maruyama and Satoru Miyano* |

| | | **Relevance Feedback using Support Vector Machines** - *Harris Drucker, Behzad Shahrary and David C. Gibbon* |

| | | **Lyapunov-Constrained Action Sets for Reinforcement Learning** - *Theodore J. Perkins and Andrew G. Barto* |

| | | **Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data** - *John Lafferty, Andrew McCallum and Fernando Pereira* |

| | | **A Generalized Representer Theorem** - *Bernhard Schölkopf, Ralf Herbrich and Alex J. Smola* |

| | | **Confirmation-Guided Discovery of First-Order Rules with Tertius** - *Peter A. Flach and Nicolas Lachiche* |

| | | **Learnability of Augmented Naive Bayes in Nominal Domains** - *Huajie Zhang and Charles X. Ling* |

| | | **A Unified Framework for Evaluation Metrics in Classification Using Decision Trees** - *Ricardo Vilalta, Mark Brodie, Daniel Oblinger and Irina Rish* |

| | | **Extraction of Primitive Motion and Discovery of Association Rules from Human Motion Data** - *Kuniaki Uehara and Mitsuomi Shimada* |

| | | **On-Line Algorithm to Predict Nearly as Well as the Best Pruning of a Decision Tree** - *Akira Maruoka and Eiji Takimoto* |

| | | **Editorial: Inductive Logic Programming is Coming of Age** - *Peter Flach and Saso Dzeroski* |

| | | **Mining from Literary Texts: Pattern Discovery and Similarity Computation** - *Masayuki Takeda, Tomoko Fukuda and Ichiro Nanri* |

| | | **Friend-or-Foe Q-learning in General-Sum Games** - *Michael L. Littman* |

| | | **On the relevance of time in neural computation and learnin** - *Wolfgang Maass* |

| | | **Proportional k-Interval Discretization for Naive-Bayes Classifiers** - *Ying Yang and Geoffrey I. Webb* |

| | | **Extraction of Signal from High Dimensional Time Series: Analysis of Ocean Bottom Seismograph Data** - *Genshiro Kitagawa, Tetsuo Takanami, Asako Kuwano, Yoshio Murai and Hideki Shimamura* |

| | | **Data-Dependent Margin-Based Generalization Bounds for Classification** - *Balázs Kégl, Tamás Linder and Gabor Lugosi* |

| | | **Top-Down Decision Tree Boosting and Its Applications** - *Eiji Takimoto and Akira Maruoka* |

| | | **Some Statistical-Estimation Methods for Stochastic Finite-State Transducers** - *David Picó and Francisco Casacuberta* |

| | | **Estimating a Boolean Perceptron from Its Average Satisfying Assignment: A Bound on the Precision Required** - *Paul W. Goldberg* |

| | | **Rule Discovery from fMRI Brain Images by Logical Regression Analysis** - *Hiroshi Tsukimoto, Mitsuru Kakimoto, Chie Morita and Yoshiaki Kikuchi* |

| | | **Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent Learning** - *Martin Zinkevich and Tucker Balch* |

| | | **Unsupervised Learning by Probabilistic Latent Semantic Analysis** - *Thomas Hofmann* |

| | | **Comprehensible Interpretation of Relief's Estimates** - *Marko Robnik-ťikonja and Igor Kononenko* |

| | | **How to Automate Neural Net Based Learning** - *Roland Linder and Siegfried J. Pöppl* |

| | | **Mining of Topographic Feature from Heterogeneous Imagery and Its Application to Lunar Craters** - *Rie Honda, Yuichi Iijima and Osamu Konishi* |

| | | **Parameter Estimation in Stochastic Logic Programs** - *James Cussens* |

| | | **A Simple Approach to Ordinal Classification** - *Eibe Frank and Mark Hall* |

| | | **Learning an Agent's Utility Function by Observing Behavior** - *Urszula Chajewska, Daphne Koller and Dirk Ormoneit* |

| | | **Gaining degrees of freedom in subsymbolic learning** - *B. Apolloni and D. Malchiodi* |

| | | **Learning of Boolean Functions Using Support Vector Machines** - *Ken Sadohara* |

| | | **Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy** - *Gerhard Widmer* |

| | | **Probabilistic inductive inference: a survey** - *Andris Ambainis* |

| | | **Continuous-Time Hierarchial Reinforcement Learning** - *Mohammad Ghavamzadeh and Sridhar Mahadevan* |

| | | **Robust Learning Is Rich** - *Sanjay Jain, Carl Smith and Rolf Wiehagen* |

| | | **A General Dimension for Exact Learning** - *José L. Balcáazar, Jorge Castro and David Guijarro* |

| | | **Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery** - *Stefan Wrobel* |

| | | **An Improved Predictive Accuracy Bound for Averaging Classifiers** - *John Langford, Matthias Seeger and Nimrod Megiddo* |

| | | **A Language-Based Similarity Measure** - *Lionel Martin and Frédéric Moal* |

| | | **Radial Basis Function Neural Networks Have Superlinear VC Dimension** - *Michael Schmitt* |

| | | **Tracking a Small Set of Experts by Mixing Past Posteriors** - *Olivier Bousquet and Manfred K. Warmuth* |

| | | **Breeding Decision Trees Using Evolutionary Techniques** - *Athanasios Papagelis and Dimitris Kalles* |

| | | **Repairing Faulty Mixture Models using Density Estimation** - *Peter Sand and Andrew W. Moore* |

| | | **Learning with the Set Covering Machine** - *Mario Marchand and John Shawe-Taylor* |

| | | **Discovery of Chances Underlying Real Data** - *Yukio Ohsawa* |

| | | **Pattern Recognition and Density Estimation under the General i.i.d Assumption** - *Ilia Nouretdinov, Volodya Vovk, Michael Vyugin and Alex Gammerman* |

| | | **Application of Neural Network Technique to Combustion Spray Dynamics Analysis** - *Yuji Ikeda and Dariusz Mazurkiewicz* |

| | | **Understanding Probabilistic Classifiers** - *Ashutosh Garg and Dan Roth* |

| | | **Learning to Select Good Title Words: A New Approach Based on Reverse Information Retrieval** - *Rong Jin and Alexander G. Hauptmann* |

| | | **A Random Sampling Technique for Training Support Vector Machines** - *Jose Balcàzar, Yang Dai and Osamu Watanabe* |

| | | **A Theory of Hypothesis Finding in Clausal Logic** - *Akihiro Yamamoto and Bertram Fronhöfer* |

| | | **Learning Intermediate Concepts** - *Stephen S. Kwek* |

| | | **Social Agents Playing a Periodical Policy** - *Ann Nowé, Johan Parent and Katja Verbeeck* |

| | | **Iterated Phantom Induction: A Knowledge-Based Approach to Learning Control** - *Mark Brodie and Gerald DeJong* |

| | | **Pairwise Comparison of Hypotheses in Evolutionary Learning** - *Krzysztof Krawiec* |

| | | **Adaptive Strategies and Regret Minimization in Arbitrarily Varying Markov Environments** - *Shie Mannor and Nahum Shimkin* |

| | | **Knowledge Discovery from Semistructured Texts** - *Hiroshi Sakamoto, Hiroki Arimura and Setsuo Arikawa* |

| | | **Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing** - *Lappoon R. Tang and Raymond J. Mooney* |

| | | **Reinterpreting the Category Utility Function** - *Boris Mirkin* |

| | | **Symbolic Discriminant Analysis for Mining Gene Expression Patterns** - *Jason H. Moore, Joel S. Parker and Lance W. Hahn* |

| | | **Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries** - *Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger and Thomas Zeugmann* |

| | | **On the Practice of Branching Program Boosting** - *Tapio Elomaa and Matti Kääriäinen* |

| | | **A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering** - *Pedro Domingos and Geoff Hulten* |

| | | **Reinforcement Learning in Dynamic Environments using Instantiated Information** - *Marco A. Wiering* |

| | | **Approximate Match of Rules Using Backpropagation Neural Networks** - *Boonserm Kijsirikul, Sukree Sinthupinyo and Kongsak Chongkasemwongse* |

| | | **Feature Selection for High-Dimensional Genomic Microarray Data** - *Eric P. Xing, Michael I. Jordan and Richard M. Karp* |

| | | **Synthesizing noise-tolerant language learners** - *John Case, Sanjay Jain and Arun Sharma* |

| | | **Statistification or Mystification? The Need for Statistical Thought in Visual Data Mining** - *Antony Unwin* |

| | | **Feature Selection for a Real-World Learning Task** - *D. Kollmar and D. H. Hellmann* |

| | | **Smooth Boosting and Learning with Malicious Noise** - *Rocco A. Servedio* |

| | | **Reinforcement Learning with Bounded Risk** - *Peter Geibel* |

| | | **Ultraconservative Online Algorithms for Multiclass Problems** - *Koby Crammer and Yoram Singer* |

| | | **Stochastic Finite Learning of the Pattern Languages** - *Peter Rossmanith and Thomas Zeugmann* |

| | | **Efficient Construction of Regression Trees with Range and Region Splitting** - *Yasuhiko Morimoto, Hiromu Ishii and Shinichi Morishita* |

| | | **Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction** - *Branko Kavsek, Nada Lavrac and Anuska Ferligoj* |

| | | **Backpropagation in Decision Trees for Regression** - *Victor Medina-Chico, Alberto Suárez and James F. Lutsko* |

| | | **Learning Relatively Small Classes** - *Shahar Mendelson* |

| | | **Discovery of Definition Patterns by Compressing Dictionary Sentences** - *Masatoshi Tsuchiya, Sadao Kurohashi and Satoshi Sato* |

| | | **Fitness Distance Correlation of Neural Network Error Surfaces: A Scalable, Continuous Optimization Problem** - *Marcus Gallagher* |

| | | **SPADE: An Efficient Algorithm for Mining Frequent Sequences** - *Mohammed J. Zaki* |

| | | **Computing Optimal Hypotheses Efficiently for Boosting** - *Shinichi Morishita* |

| | | **The Structure of Scientific Discovery: From a Philosophical Point of View** - *Keiichi Noé* |

| | | **Some Sparse Approximation Bounds for Regression Problems** - *Tong Zhang* |

| | | **Improving the Robustness and Encoding Complexity of Behavioural Clones** - *Rui Camacho and Pavel Brazdil* |

| | | **Improved Bounds on the Sample Complexity of Learning** - *Yi Li, Philip M. Long and Aravind Srinivasan* |

| | | **Some Criterions for Selecting the Best Data Abstractions** - *Makoto Haraguchi and Yoshimitsu Kudoh* |

| January | | **On learning formulas in the limit and with assurance** - *Andris Ambainis* |

| April | | **On an open problem in classification of languages** - *Sanjay Jain* |

| May | | **Improved Lower Bounds for Learning from Noisy Examples: An Information-Theoretic Approach** - *Claudio Gentile and David P. Helmbold* |

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

| September | | **Most Sequences Are Stochastic** - *V. V. V'yugin* |

| November | | **The Query Complexity of Finding Local Minima in the Lattice** - *Amos Beimel, Felix Geller and Eyal Kushilevitz* |

| | | **Learning Fixed-Dimension Linear Thresholds from Fragmented Data** - *Paul W. Goldberg* |

| | | **Algorithmic Learning Theory, 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001, Proceedings** - *Naoki Abe and Roni Khardon and Thomas Zeugmann* |

| | | **Induction by Enumeration** - *Eric Martin and Daniel Osherson* |