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

• 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
  

• 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

• 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
  

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