| 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 |
| | | WBCSVM: 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 |
