Journal of Machine Learning Research, Volume 3

Volume 3, July 2002

• 
  Francis R. Bach, Michael I. Jordan:
  Kernel Independent Component Analysis. 1-48
• 
  Nader H. Bshouty, Nadav Eiron:
  Learning Monotone DNF from a Teacher that Almost Does Not Answer Membership Queries. 49-57
• 
  John N. Tsitsiklis:
  On the Convergence of Optimistic Policy Iteration. 59-72
• 
  András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi:
  Data-dependent margin-based generalization bounds for classification. 73-98

Volume 3, August 2002

• 
  Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski:
  Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components. 99-114
• 
  Felix A. Gers, Nicol N. Schraudolph, Jürgen Schmidhuber:
  Learning Precise Timing with LSTM Recurrent Networks. 115-143
• 
  Istvan Szita, Bálint Takács, András Lörincz:
  MDPs: Learning in Varying Environments. 145-174

Volume 3, September 2002

• 
  Ralf Herbrich, Robert C. Williamson:
  Algorithmic Luckiness. 175-212

Volume 3, October 2002

• 
  Ronen I. Brafman, Moshe Tennenholtz:
  R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning. 213-231
• 
  Matthias W. Seeger:
  PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification. 233-269
• 
  Ziv Nevo, Ran El-Yaniv:
  On Online Learning of Decision Lists. 271-301

Volume 3, November 2002

• 
  Glenn Fung, Olvi L. Mangasarian, Alexander J. Smola:
  Minimal Kernel Classifiers. 303-321
• 
  Masashi Sugiyama, Klaus-Robert Müller:
  The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces. 323-359

Special Issue on Computational Learning Theory

• 
  Olivier Bousquet, Manfred K. Warmuth:
  Tracking a Small Set of Experts by Mixing Past Posteriors. 363-396
• 
  Peter Auer:
  Using Confidence Bounds for Exploitation-Exploration Trade-offs. 397-422
• 
  Adam Kalai, Santosh Vempala:
  Efficient Algorithms for Universal Portfolios. 423-440
• 
  Shai Ben-David, Nadav Eiron, Hans Ulrich Simon:
  Limitations of Learning Via Embeddings in Euclidean Half Spaces. 441-461
• 
  Peter L. Bartlett, Shahar Mendelson:
  Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. 463-482
• 
  Nader H. Bshouty, Dmitry Gavinsky:
  On Boosting with Polynomially Bounded Distributions. 483-506
• 
  David Maxwell Chickering:
  Optimal Structure Identification With Greedy Search. 507-554

Volume 3, December 2002

• 
  Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan:
  A Robust Minimax Approach to Classification. 555-582
• 
  Alexander Strehl, Joydeep Ghosh:
  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions. 583-617

Special Issue on the Eighteenth International Conference on Machine Learning (ICML2001)

• 
  Hendrik Blockeel, Jan Struyf:
  Efficient Algorithms for Decision Tree Cross-validation. 621-650
• 
  Daniel R. Dooly, Qi Zhang, Sally A. Goldman, Robert A. Amar:
  Multiple-Instance Learning of Real-Valued Data. 651-678
• 
  Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar:
  Learning Probabilistic Models of Link Structure. 679-707
• 
  Charles X. Ling, Huajie Zhang:
  The Representational Power of Discrete Bayesian Networks. 709-721
• 
  Mario Marchand, John Shawe-Taylor:
  The Set Covering Machine. 723-746
• 
  Zvika Marx, Ido Dagan, Joachim M. Buhmann, Eli Shamir:
  Coupled Clustering: A Method for Detecting Structural Correspondence. 747-780
• 
  Prasanth B. Nair, Arindam Choudhury, Andy J. Keane:
  Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels. 781-801
• 
  Theodore J. Perkins, Andrew G. Barto:
  Lyapunov Design for Safe Reinforcement Learning. 803-832
• 
  Tobias Scheffer, Stefan Wrobel:
  Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling. 833-862
• 
  Marc Sebban, Richard Nock, Stéphane Lallich:
  Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem. 863-885
• 
  Bryan Singer, Manuela M. Veloso:
  Learning to Construct Fast Signal Processing Implementations. 887-919
• 
  Malcolm J. A. Strens, Andrew W. Moore:
  Policy Search using Paired Comparisons. 921-950

Volume 3, January 2003

• 
  Koby Crammer, Yoram Singer:
  Ultraconservative Online Algorithms for Multiclass Problems. 951-991
• 
  David M. Blei, Andrew Y. Ng, Michael I. Jordan:
  Latent Dirichlet Allocation. 993-1022

Volume 3, Febuary 2003

Special Issue on Machine Learning Methods for Text and Images

• 
  Koby Crammer, Yoram Singer:
  A Family of Additive Online Algorithms for Category Ranking. 1025-1058
• 
  Nicola Cancedda, Éric Gaussier, Cyril Goutte, Jean-Michel Renders:
  Word-Sequence Kernels. 1059-1082
• 
  Dmitry Zelenko, Chinatsu Aone, Anthony Richardella:
  Kernel Methods for Relation Extraction. 1083-1106
• 
  Kobus Barnard, Pinar Duygulu, David A. Forsyth, Nando de Freitas, David M. Blei, Michael I. Jordan:
  Matching Words and Pictures. 1107-1135
• 
  Yoshua Bengio, Réjean Ducharme, Pascal Vincent, Christian Janvin:
  A Neural Probabilistic Language Model. 1137-1155

Volume 3, March 2003

• 
  Isabelle Guyon, André Elisseeff:
  An Introduction to Variable and Feature Selection. 1157-1182
• 
  Ron Bekkerman, Ran El-Yaniv, Naftali Tishby, Yoad Winter:
  Distributional Word Clusters vs. Words for Text Categorization. 1183-1208
• 
  Yoshua Bengio, Nicolas Chapados:
  Extensions to Metric-Based Model Selection. 1209-1227
• 
  Jinbo Bi, Kristin P. Bennett, Mark J. Embrechts, Curt M. Breneman, Minghu Song:
  Dimensionality Reduction via Sparse Support Vector Machines. 1229-1243
• 
  Rich Caruana, Virginia R. de Sa:
  Benefitting from the Variables that Variable Selection Discards. 1245-1264
• 
  Inderjit S. Dhillon, Subramanyam Mallela, Rahul Kumar:
  A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification. 1265-1287
• 
  George Forman:
  An Extensive Empirical Study of Feature Selection Metrics for Text Classification. 1289-1305
• 
  Amir Globerson, Naftali Tishby:
  Sufficient Dimensionality Reduction. 1307-1331
• 
  Simon Perkins, Kevin Lacker, James Theiler:
  Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space. 1333-1356
• 
  Alain Rakotomamonjy:
  Variable Selection Using SVM-based Criteria. 1357-1370
• 
  Juha Reunanen:
  Overfitting in Making Comparisons Between Variable Selection Methods. 1371-1382
• 
  Isabelle Rivals, Léon Personnaz:
  MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling. 1383-1398
• 
  Hervé Stoppiglia, Gérard Dreyfus, Rémi Dubois, Yacine Oussar:
  Ranking a Random Feature for Variable and Feature Selection. 1399-1414
• 
  Kari Torkkola:
  Feature Extraction by Non-Parametric Mutual Information Maximization. 1415-1438
• 
  Jason Weston, André Elisseeff, Bernhard Schölkopf, Michael E. Tipping:
  Use of the Zero-Norm with Linear Models and Kernel Methods. 1439-1461