START JMLR Volume 1

JMLR Volume 1

Learning with Mixtures of Trees: Marina Meila, Michael I. Jordan; 1(Oct):1-48, 2000.
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Dependency Networks for Inference, Collaborative Filtering, and Data Visualization: David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Kadie; 1(Oct):49-75, 2000.
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Learning Evaluation Functions to Improve Optimization by Local Search: Justin Boyan, Andrew W. Moore; 1(Nov):77-112, 2000.
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Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers: Erin L. Allwein, Robert E. Schapire, Yoram Singer; 1(Dec):113-141, 2000.
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SVMTorch: Support Vector Machines for Large-Scale Regression Problems (Kernel Machines Section): Ronan Collobert, Samy Bengio; 1(Feb):143-160, 2001.
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Lagrangian Support Vector Machines (Kernel Machines Section): O. L. Mangasarian, David R. Musicant; 1(Mar):161-177, 2001.
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Regularized Principal Manifolds (Kernel Machines Section): Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson; 1(Jun):179-209, 2001.
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Sparse Bayesian Learning and the Relevance Vector Machine: Michael E. Tipping; 1(Jun):211-244, 2001.
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Bayes Point Machines (Kernel Machines Section): Ralf Herbrich, Thore Graepel, Colin Campbell; 1(Aug):245-279, 2001.
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Tracking the Best Linear Predictor: Mark Herbster, Manfred K. Warmuth; 1(Sep):281-309, 2001.
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Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms: Robert E. Mahony, Robert C. Williamson; 1(Sep):311-355, 2001.
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JMLR Volume 2

On the Size of Convex Hulls of Small Sets: Shahar Mendelson; 2(Oct):1-18, 2001.
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Graph-Based Hierarchical Conceptual Clustering: Istvan Jonyer, Diane J. Cook, Lawrence B. Holder; 2(Oct):19-43, 2001.
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Support Vector Machine Active Learning with Applications to Text Classification: Simon Tong, Daphne Koller; 2(Nov):45-66, 2001.
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On the Influence of the Kernel on the Consistency of Support Vector Machines (Kernel Machines Section): Ingo Steinwart; 2(Nov):67-93, 2001.
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Introduction to the Special Issue on Kernel Methods (Kernel Machines Section): Nello Cristianini, John Shawe-Taylor, Robert C. Williamson; 2(Dec):95-96, 2001.
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Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space (Kernel Machines Section): Roman Rosipal, Leonard J. Trejo; 2(Dec):97-123, 2001.
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Support Vector Clustering (Kernel Machines Section): Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik; 2(Dec):125-137, 2001.
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revision: Jan 2002
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One-Class SVMs for Document Classification (Kernel Machines Section): Larry M. Manevitz, Malik Yousef; 2(Dec):139-154, 2001.
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Uniform Object Generation for Optimizing One-class Classifiers (Kernel Machines Section): David M.J. Tax, Robert P.W. Duin; 2(Dec):155-173, 2001.
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A Generalized Kernel Approach to Dissimilarity-based Classification (Kernel Machines Section): Elzbieta Pekalska, Pavel Paclik, Robert P.W. Duin; 2(Dec):175-211, 2001.
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revision: Jan 2002
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A New Approximate Maximal Margin Classification Algorithm (Kernel Machines Section): Claudio Gentile; 2(Dec):213-242, 2001.
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Efficient SVM Training Using Low-Rank Kernel Representations (Kernel Machines Section): Shai Fine, Katya Scheinberg; 2(Dec):243-264, 2001.
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On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines (Kernel Machines Section): Koby Crammer, Yoram Singer; 2(Dec):265-292, 2001.
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Exact Simplification of Support Vector Solutions (Kernel Machines Section): Tom Downs, Kevin E. Gates, Annette Masters; 2(Dec):293-297, 2001.
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Classes of Kernels for Machine Learning: A Statistics Perspective (Kernel Machines Section): Marc G. Genton; 2(Dec):299-312, 2001.
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Recommender Systems Using Linear Classifiers: Tong Zhang, Vijay S. Iyengar; 2(Feb):313-334, 2002.
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Machine Learning with Data Dependent Hypothesis Classes: Adam Cannon, J. Mark Ettinger, Don Hush, Clint Scovel; 2(Feb):335-358, 2002.
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On Using Extended Statistical Queries to Avoid Membership Queries: Nader H. Bshouty, Vitaly Feldman; 2(Feb):359-395, 2002.
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The Learning-Curve Sampling Method Applied to Model-Based Clustering: Christopher Meek, Bo Thiesson, David Heckerman; 2(Feb):397-418, 2002.
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Text Classification using String Kernels: Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Chris Watkins; 2(Feb):419-444, 2002.
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Learning Equivalence Classes of Bayesian-Network Structures: David Maxwell Chickering; 2(Feb):445-498, 2002.
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Stability and Generalization: Olivier Bousquet, André Elisseeff; 2(Mar):499-526, 2002.
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Covering Number Bounds of Certain Regularized Linear Function Classes: Tong Zhang; 2(Mar):527-550, 2002.
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Introduction to Special Issue on Machine Learning Approaches to Shallow Parsing: James Hammerton, Miles Osborne, Susan Armstrong, Walter Daelemans; 2(Mar):551-558, 2002.
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Memory-Based Shallow Parsing: Erik F. Tjong Kim Sang; 2(Mar):559-594, 2002.
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Shallow Parsing using Specialized HMMs: Antonio Molina, Ferran Pla; 2(Mar):595-613, 2002.
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Text Chunking based on a Generalization of Winnow: Tong Zhang, Fred Damerau, David Johnson; 2(NIL):615-637, 3.
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Shallow Parsing with PoS Taggers and Linguistic Features: Beáta Megyesi; 2(Mar):639-668, 2002.
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Learning Rules and Their Exceptions: Hervé Déjean; 2(Mar):669-693, 2002.
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Shallow Parsing using Noisy and Non-Stationary Training Material: Miles Osborne; 2(Mar):695-719, 2002.
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Round Robin Classification: Johannes Fürnkranz; 2(Mar):721-747, 2002.
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JMLR Volume 3

Kernel Independent Component Analysis (Kernel Machines Section): Francis R. Bach, Michael I. Jordan; 3(Jul):1-48, 2002.
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Learning Monotone DNF from a Teacher that Almost Does Not Answer Membership Queries: Nader H. Bshouty, Nadav Eiron; 3(Jul):49-57, 2002.
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On the Convergence of Optimistic Policy Iteration: John N. Tsitsiklis; 3(Jul):59-72, 2002.
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Data-dependent margin-based generalization bounds for classification: András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi; 3(Jul):73-98, 2002.
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Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components: Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski; 3(Aug):99-114, 2002.
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Learning Precise Timing with LSTM Recurrent Networks: Felix A. Gers, Nicol N. Schraudolph, Jürgen Schmidhuber; 3(Aug):115-143, 2002.
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ε-MDPs: Learning in Varying Environments: István Szita, Bálint Takács, András Lörincz; 3(Aug):145-174, 2002.
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Algorithmic Luckiness: Ralf Herbrich, Robert C. Williamson; 3(Sep):175-212, 2002.
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R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning: Ronen I. Brafman, Moshe Tennenholtz; 3(Oct):213-231, 2002.
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PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification (Kernel Machines Section): Matthias Seeger; 3(Oct):233-269, 2002.
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On Online Learning of Decision Lists: Ziv Nevo, Ran El-Yaniv; 3(Oct):271-301, 2002.
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Minimal Kernel Classifiers (Kernel Machines Section): Glenn M. Fung, Olvi L. Mangasarian, Alexander J. Smola; 3(Nov):303-321, 2002.
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The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces (Kernel Machines Section): Masashi Sugiyama, Klaus-Robert Müller; 3(Nov):323-359, 2002.
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Introduction to the Special Issue on Computational Learning Theory: Philip M. Long; 3(Nov):361-362, 2002.
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Tracking a Small Set of Experts by Mixing Past Posteriors: Olivier Bousquet, Manfred K. Warmuth; 3(Nov):363-396, 2002.
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Using Confidence Bounds for Exploitation-Exploration Trade-offs: Peter Auer; 3(Nov):397-422, 2002.
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Efficient Algorithms for Universal Portfolios: Adam Kalai, Santosh Vempala; 3(Nov):423-440, 2002.
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Limitations of Learning Via Embeddings in Euclidean Half Spaces: Shai Ben-David, Nadav Eiron, Hans Ulrich Simon; 3(Nov):441-461, 2002.
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Rademacher and Gaussian Complexities: Risk Bounds and Structural Results: Peter L. Bartlett, Shahar Mendelson; 3(Nov):463-482, 2002.
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On Boosting with Polynomially Bounded Distributions: Nader H. Bshouty, Dmitry Gavinsky; 3(Nov):483-506, 2002.
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Optimal Structure Identification With Greedy Search: David Maxwell Chickering; 3(Nov):507-554, 2002.
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A Robust Minimax Approach to Classification: Gert R.G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan; 3(Dec):555-582, 2002.
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Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions: Alexander Strehl, Joydeep Ghosh; 3(Dec):583-617, 2002.
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Special Issue on the Eighteenth International Conference on Machine Learning (ICML2001): Carla E. Brodley, Andrea P. Danyluk; 3(Dec):619-620, 2002.
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Efficient Algorithms for Decision Tree Cross-validation: Hendrik Blockeel, Jan Struyf; 3(Dec):621-650, 2002.
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Multiple-Instance Learning of Real-Valued Data: Daniel R. Dooly, Qi Zhang, Sally A. Goldman, Robert A. Amar; 3(Dec):651-678, 2002.
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Learning Probabilistic Models of Link Structure: Lisa Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar; 3(Dec):679-707, 2002.
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The Representational Power of Discrete Bayesian Networks: Charles X. Ling, Huajie Zhang; 3(Dec):709-721, 2002.
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The Set Covering Machine: Mario Marchand, John Shawe-Taylor; 3(Dec):723-746, 2002.
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Coupled Clustering: A Method for Detecting Structural Correspondence: Zvika Marx, Ido Dagan, Joachim M. Buhmann, Eli Shamir; 3(Dec):747-780, 2002.
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Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels: Prasanth B. Nair, Arindam Choudhury, Andy J. Keane; 3(Dec):781-801, 2002.
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Lyapunov Design for Safe Reinforcement Learning: Theodore J. Perkins, Andrew G. Barto; 3(Dec):803-832, 2002.
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Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling: Tobias Scheffer, Stefan Wrobel; 3(Dec):833-862, 2002.
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Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem: Marc Sebban, Richard Nock, Stéphane Lallich; 3(Dec):863-885, 2002.
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Learning to Construct Fast Signal Processing Implementations: Bryan Singer, Manuela Veloso; 3(Dec):887-919, 2002.
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Policy Search using Paired Comparisons: Malcolm J. A. Strens, Andrew W. Moore; 3(Dec):921-950, 2002.
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Ultraconservative Online Algorithms for Multiclass Problems: Koby Crammer, Yoram Singer; 3(Jan):951-991, 2003.
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Latent Dirichlet Allocation: David M. Blei, Andrew Y. Ng, Michael I. Jordan; 3(Jan):993-1022, 2003.
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Introduction to the Special Issue on Machine Learning Methods for Text and Images: Jaz Kandola, Thomas Hofmann, Tomaso Poggio, John Shawe-Taylor; 3(Feb):1023-1024, 2003.
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A Family of Additive Online Algorithms for Category Ranking: Koby Crammer, Yoram Singer; 3(Feb):1025-1058, 2003.
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Word-Sequence Kernels: Nicola Cancedda, Eric Gaussier, Cyril Goutte, Jean-Michel Renders; 3(Feb):1059-1082, 2003.
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Kernel Methods for Relation Extraction: Dmitry Zelenko, Chinatsu Aone, Anthony Richardella; 3(Feb):1083-1106, 2003.
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Matching Words and Pictures: Kobus Barnard, Pinar Duygulu, David Forsyth, Nando de Freitas,David M. Blei, Michael I. Jordan; 3(Feb):1107-1135, 2003.
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A Neural Probabilistic Language Model: Yoshua Bengio, Réjean Ducharme, Pascal Vincent, Christian Jauvin; 3(Feb):1137-1155, 2003.
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An Introduction to Variable and Feature Selection (Kernel Machines Section): Isabelle Guyon, André Elisseeff; 3(Mar):1157-1182, 2003.
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Distributional Word Clusters vs. Words for Text Categorization (Kernel Machines Section): Ron Bekkerman, Ran El-Yaniv, Naftali Tishby, Yoad Winter; 3(Mar):1183-1208, 2003.
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Extensions to Metric-Based Model Selection: Yoshua Bengio, Nicolas Chapados; 3(Mar):1209-1227, 2003.
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Dimensionality Reduction via Sparse Support Vector Machines (Kernel Machines Section): Jinbo Bi, Kristin Bennett, Mark Embrechts, Curt Breneman, Minghu Song; 3(Mar):1229-1243, 2003.
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Benefitting from the Variables that Variable Selection Discards: Rich Caruana, Virginia R. de Sa; 3(Mar):1245-1264, 2003.
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A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification (Kernel Machines Section): Inderjit S. Dhillon, Subramanyam Mallela, Rahul Kumar; 3(Mar):1265-1287, 2003.
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An Extensive Empirical Study of Feature Selection Metrics for Text Classification (Kernel Machines Section): George Forman; 3(Mar):1289-1305, 2003.
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Sufficient Dimensionality Reduction (Kernel Machines Section): Amir Globerson, Naftali Tishby; 3(Mar):1307-1331, 2003.
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Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space (Kernel Machines Section): Simon Perkins, Kevin Lacker, James Theiler; 3(Mar):1333-1356, 2003.
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Variable Selection Using SVM-based Criteria (Kernel Machines Section): Alain Rakotomamonjy; 3(Mar):1357-1370, 2003.
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Overfitting in Making Comparisons Between Variable Selection Methods: Juha Reunanen; 3(Mar):1371-1382, 2003.
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MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling: Isabelle Rivals, Léon Personnaz; 3(Mar):1383-1398, 2003.
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Ranking a Random Feature for Variable and Feature Selection: Hervé Stoppiglia, Gérard Dreyfus, Rémi Dubois, Yacine Oussar; 3(Mar):1399-1414, 2003.
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Feature Extraction by Non-Parametric Mutual Information Maximization (Kernel Machines Section): Kari Torkkola; 3(Mar):1415-1438, 2003.
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Use of the Zero-Norm with Linear Models and Kernel Methods (Kernel Machines Section): Jason Weston, André Elisseeff, Bernhard Schölkopf, Mike Tipping; 3(Mar):1439-1461, 2003.
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JMLR Volume 4

On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines: Aldebaro Klautau, Nikola Jevtić, Alon Orlitsky; 4(Apr):1-15, 2003.
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FINkNN: A Fuzzy Interval Number k-Nearest Neighbor Classifier for Prediction of Sugar Production from Populations of Samples: Vassilios Petridis, Vassilis G. Kaburlasos; 4(Apr):17-37, 2003.
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Designing Committees of Models through Deliberate Weighting of Data Points: Stefan W. Christensen, Ian Sinclair, Philippa A. S. Reed; 4(Apr):39-66, 2003.
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The em Algorithm for Kernel Matrix Completion with Auxiliary Data: Koji Tsuda, Shotaro Akaho, Kiyoshi Asai; 4(May):67-81, 2003.
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Task Clustering and Gating for Bayesian Multitask Learning: Bart Bakker, Tom Heskes; 4(May):83-99, 2003.
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Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning: Dmitry Gavinsky; 4(May):101-117, 2003.
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Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds: Lawrence K. Saul, Sam T. Roweis; 4(Jun):119-155, 2003.
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On the Proper Learning of Axis-Parallel Concepts: Nader H. Bshouty, Lynn Burroughs; 4(Jun):157-176, 2003.
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Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction: Mary Elaine Califf, Raymond J. Mooney; 4(Jun):177-210, 2003.
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Tree Induction vs. Logistic Regression: A Learning-Curve Analysis: Claudia Perlich, Foster Provost, Jeffrey S. Simonoff; 4(Jun):211-255, 2003.
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Learning Probabilistic Models: An Expected Utility Maximization Approach: Craig Friedman, Sven Sandow; 4(Jul):257-291, 2003.
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Introduction to the Special Issue on the Fusion of Domain Knowledge with Data for Decision Support: Richard Dybowski, Kathryn B. Laskey, James W. Myers, Simon Parsons; 4(Jul):293-294, 2003.
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Combining Knowledge from Different Sources in Causal Probabilistic Models: Marek J. Druzdzel, Francisco J. Díez; 4(Jul):295-316, 2003.
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Preference Elicitation via Theory Refinement: Peter Haddawy, Vu Ha, Angelo Restificar, Benjamin Geisler, John Miyamoto; 4(Jul):317-337, 2003.
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Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains: Helge Langseth, Thomas D. Nielsen; 4(Jul):339-368, 2003.
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An Empirical Study of the Use of Relevance Information in Inductive Logic Programming: Ashwin Srinivasan, Ross D. King, Michael E. Bain; 4(Jul):369-383, 2003.
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revision: Aug 2003
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Learning Behavior-Selection by Emotions and Cognition in a Multi-Goal Robot Task: Sandra Clara Gadanho; 4(Jul):385-412, 2003.
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Introduction to the Special Issue on Inductive Logic Programming: James Cussens, Alan M. Frisch; 4(Aug):413-414, 2003.
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ILP: A Short Look Back and a Longer Look Forward: David Page, Ashwin Srinivasan; 4(Aug):415-430, 2003.
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Relational Learning as Search in a Critical Region: Marco Botta, Attilio Giordana, Lorenza Saitta, Michèle Sebag; 4(Aug):431-463, 2003.
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Query Transformations for Improving the Efficiency of ILP Systems: Vítor Santos Costa, Ashwin Srinivasan, Rui Camacho, Hendrik Blockeel, Bart Demoen, Gerda Janssens, Jan Struyf, Henk Vandecasteele, Wim Van Laer; 4(Aug):465-491, 2003.
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Learning Semantic Lexicons from a Part-of-Speech and Semantically Tagged Corpus Using Inductive Logic Programming: Vincent Claveau, Pascale Sébillot, Cécile Fabre, Pierrette Bouillon; 4(Aug):493-525, 2003.
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On Inclusion-Driven Learning of Bayesian Networks: Robert Castelo, Tomás Kocka; 4(Sep):527-574, 2003.
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The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem: Pierre Baldi, Gianluca Pollastri; 4(Sep):575-602, 2003.
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Inducing Grammars from Sparse Data Sets: A Survey of Algorithms and Results: Orlando Cicchello, Stefan C. Kremer; 4(Oct):603-632, 2003.
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Smooth Boosting and Learning with Malicious Noise: Rocco A. Servedio; 4(Sep):633-648, 2003.
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Speedup Learning for Repair-based Search by Identifying Redundant Steps: Shaul Markovitch, Asaf Shatil; 4(Sep):649-682, 2003.
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Comparing Bayes Model Averaging and Stacking When Model Approximation Error Cannot be Ignored: Bertrand Clarke; 4(Oct):683-712, 2003.
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Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity: Shie Mannor, Ron Meir, Tong Zhang; 4(Oct):713-742, 2003.
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Tree-Structured Neural Decoding: Christian d'Avignon, Donald Geman; 4(Oct):743-754, 2003.
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Introduction to the Special Issue on Learning Theory: Ralf Herbrich, Thore Graepel; 4(Oct):755-757, 2003.
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On the Performance of Kernel Classes: Shahar Mendelson; 4(Oct):759-771, 2003.
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Path Kernels and Multiplicative Updates: Eiji Takimoto, Manfred K. Warmuth; 4(Oct):773-818, 2003.
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Tracking Linear-threshold Concepts with Winnow: Chris Mesterharm; 4(Oct):819-838, 2003.
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Generalization Error Bounds for Bayesian Mixture Algorithms: Ron Meir, Tong Zhang; 4(Oct):839-860, 2003.
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On the Rate of Convergence of Regularized Boosting Classifiers: Gilles Blanchard, Gábor Lugosi, Nicolas Vayatis; 4(Oct):861-894, 2003.
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Concentration Inequalities for the Missing Mass and for Histogram Rule Error: David McAllester, Luis Ortiz; 4(Oct):895-911, 2003.
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Learning over Sets using Kernel Principal Angles (Kernel Machines Section): Lior Wolf, Amnon Shashua; 4(Oct):913-931, 2003.
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An Efficient Boosting Algorithm for Combining Preferences: Yoav Freund, Raj Iyer, Robert E. Schapire, Yoram Singer; 4(Nov):933-969, 2003.
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Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet: Marcus Hutter; 4(Nov):971-1000, 2003.
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A Unified Framework for Model-based Clustering: Shi Zhong, Joydeep Ghosh; 4(Nov):1001-1037, 2003.
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Nash Q-Learning for General-Sum Stochastic Games: Junling Hu, Michael P. Wellman; 4(Nov):1039-1069, 2003.
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Sparseness of Support Vector Machines: Ingo Steinwart; 4(Nov):1071-1105, 2003.
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Least-Squares Policy Iteration: Michail G. Lagoudakis, Ronald Parr; 4(Dec):1107-1149, 2003.
[abs][pdf][ps.gz][ps]

An Approximate Analytical Approach to Resampling Averages (Kernel Machines Section): Dörthe Malzahn, Manfred Opper; 4(Dec):1151-1173, 2003.
[abs][pdf][ps.gz][ps]

Introduction to Special Issue on Independent Components Analysis: Te-Won Lee, Jean-François Cardoso, Erkki Oja, Shun-ichi Amari; 4(Dec):1175-1176, 2003.
[abs]][pdf][ps.gz][ps]

Dependence, Correlation and Gaussianity in Independent Component Analysis: Jean-François Cardoso; 4(Dec):1177-1203, 2003.
[abs][pdf][ps.gz][ps]

Beyond Independent Components: Trees and Clusters: Francis R. Bach, Michael I. Jordan; 4(Dec):1205-1233, 2003.
[abs][pdf][ps.gz][ps]

Energy-Based Models for Sparse Overcomplete Representations: Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton; 4(Dec):1235-1260, 2003.
[abs][pdf][ps.gz][ps]

Blind Source Separation via Generalized Eigenvalue Decomposition: Lucas Parra, Paul Sajda; 4(Dec):1261-1269, 2003.
[abs][pdf][ps.gz][ps]

ICA Using Spacings Estimates of Entropy: Erik G. Learned-Miller, John W. Fisher III; 4(Dec):1271-1295, 2003.
[abs][pdf][ps.gz][ps]

MISEP -- Linear and Nonlinear ICA Based on Mutual Information: Luís B. Almeida; 4(Dec):1297-1318, 2003.
[abs][pdf][ps.gz][ps]

Blind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation: Andreas Ziehe, Motoaki Kawanabe, Stefan Harmeling, Klaus-Robert Müller; 4(Dec):1319-1338, 2003.
[abs][pdf][ps.gz][ps]

A Multiscale Framework For Blind Separation of Linearly Mixed Signals: Pavel Kisilev, Michael Zibulevsky, Yehoshua Y. Zeevi; 4(Dec):1339-1363, 2003.
[abs][pdf][ps.gz][ps]

A Maximum Likelihood Approach to Single-channel Source Separation: Gil-Jin Jang, Te-Won Lee; 4(Dec):1365-1392, 2003.
[abs][pdf][ps.gz][ps]

Statistical Dynamics of On-line Independent Component Analysis: Gleb Basalyga, Magnus Rattray; 4(Dec):1393-1410, 2003.
[abs][pdf][ps.gz][ps]

Blind Source Recovery: A Framework in the State Space: Khurram Waheed, Fathi M. Salem; 4(Dec):1411-1446, 2003.
[abs][pdf][ps.gz][ps]

Overlearning in Marginal Distribution-Based ICA: Analysis and Solutions: Jaakko Särelä, Ricardo Vigário; 4(Dec):1447-1469, 2003.
[abs][pdf][ps.gz][ps]

ICA for Watermarking Digital Images: Stéphane Bounkong, Borémi Toch, David Saad, David Lowe; 4(Dec):1471-1498, 2003.
[abs][pdf][ps.gz][ps]

A Generative Model for Separating Illumination and Reflectance from Images: Inna Stainvas, David Lowe; 4(Dec):1499-1519, 2003.
[abs][pdf][ps.gz][ps]

JMLR Volume 5

Learning Rates for Q-learning: Eyal Even Dar, Yishay Mansour; 5(Dec):1--25, 2003.
[abs] [pdf] [ps.gz] [ps]

Learning the Kernel Matrix with Semidefinite Programming: Gert R.G. Lanckriet, Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, Michael I. Jordan; 5(Jan):27--72, 2004.
[abs] [pdf] [ps.gz] [ps]

Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces: Kenji Fukumizu, Francis R. Bach, Michael I. Jordan; 5(Jan):73--99, 2004.
[abs] [pdf] [ps.gz] [ps]

In Defense of One-Vs-All Classification: Ryan Rifkin, Aldebaro Klautau; 5(Jan):101--141, 2004.
[abs] [pdf] [ps.gz] [ps]

Lossless Online Bayesian Bagging: Herbert K. H. Lee, Merlise A. Clyde; 5(Feb):143--151, 2004.
[abs] [pdf] [ps.gz] [ps]

Subgroup Discovery with CN2-SD: Nada Lavrač, Branko Kavšek, Peter Flach, Ljupčo Todorovski; 5(Feb):153--188, 2004.
[abs] [pdf] [ps.gz] [ps]

Generalization Error Bounds for Threshold Decision Lists: Martin Anthony; 5(Feb):189--217, 2004.
[abs] [pdf] [ps.gz] [ps]

On the Importance of Small Coordinate Projections: Shahar Mendelson, Petra Philips; 5(Mar):219--238, 2004.
[abs] [pdf] [ps.gz] [ps]

Weather Data Mining Using Independent Component Analysis: Jayanta Basak, Anant Sudarshan, Deepak Trivedi, M. S. Santhanam; 5(Mar):239--253, 2004.
[abs] [pdf] [ps.gz] [ps]

Online Choice of Active Learning Algorithms: Yoram Baram, Ran El Yaniv, Kobi Luz; 5(Mar):255--291, 2004.
[abs] [pdf] [ps.gz] [ps]

A Compression Approach to Support Vector Model Selection: Ulrike von Luxburg, Olivier Bousquet, Bernhard Schölkopf; 5(Apr):293--323, 2004.
[abs] [pdf] [ps.gz] [ps]

A Geometric Approach to Multi-Criterion Reinforcement Learning: Shie Mannor, Nahum Shimkin; 5(Apr):325--360, 2004.
[abs] [pdf] [ps.gz] [ps]

RCV1: A New Benchmark Collection for Text Categorization Research: David D. Lewis, Yiming Yang, Tony G. Rose, Fan Li; 5(Apr):361--397, 2004.
[abs] [pdf] [ps.gz] [ps] [appendices]

Distributional Scaling: An Algorithm for Structure-Preserving Embedding of Metric and Nonmetric Spaces: Michael Quist, Golan Yona; 5(Apr):399--420j, 2004.
[abs] [pdf] [ps.gz] [ps]

Learning Ensembles from Bites: A Scalable and Accurate Approach: Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer; 5(Apr):421--451, 2004.
[abs] [pdf] [ps.gz] [ps]

Robust Principal Component Analysis with Adaptive Selection for Tuning Parameters: Isao Higuchi, Shinto Eguchi; 5(May):453--471, 2004.
[abs] [pdf] [ps.gz] [ps]

PAC-learnability of Probabilistic Deterministic Finite State Automata: Alexander Clark, Franck Thollard; 5(May):473--497, 2004.
[abs] [pdf] [ps.gz] [ps]

Sources of Success for Boosted Wrapper Induction: David Kauchak, Joseph Smarr, Charles Elkan; 5(May):499--527, 2004.
[abs] [pdf] [ps.gz] [ps]

Computable Shell Decomposition Bounds: John Langford, David McAllester; 5(May):529--547, 2004.
[abs] [pdf] [ps.gz] [ps]

Exact Bayesian Structure Discovery in Bayesian Networks: Mikko Koivisto, Kismat Sood; 5(May):549--573, 2004.
[abs] [pdf] [ps.gz] [ps]

A Universal Well-Calibrated Algorithm for On-line Classification (Special Topic on Learning Theory): Vladimir Vovk; 5(Jun):575--604, 2004.
[abs] [pdf] [ps.gz] [ps]

New Techniques for Disambiguation in Natural Language and Their Application to Biological Text: Filip Ginter, Jorma Boberg, Jouni Järvinen, Tapio Salakoski; 5(Jun):605--621, 2004.
[abs] [pdf] [ps.gz] [ps]

The Sample Complexity of Exploration in the Multi-Armed Bandit Problem (Special Topic on Learning Theory): Shie Mannor, John N. Tsitsiklis; 5(Jun):623--648, 2004.
[abs] [pdf] [ps.gz] [ps]

Preference Elicitation and Query Learning (Special Topic on Learning Theory): Avrim Blum, Jeffrey Jackson, Tuomas Sandholm, Martin Zinkevich; 5(Jun):649--667, 2004.
[abs] [pdf] [ps.gz] [ps]

Distance-Based Classification with Lipschitz Functions (Special Topic on Learning Theory): Ulrike von Luxburg, Olivier Bousquet; 5(Jun):669--695, 2004.
[abs] [pdf] [ps.gz] [ps]

Hierarchical Latent Class Models for Cluster Analysis: Nevin L. Zhang; 5(Jun):697--723, 2004.
[abs] [pdf] [ps.gz] [ps]

Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods: Giorgio Valentini, Thomas G. Dietterich; 5(Jul):725--775, 2004.
[abs][pdf] [ps.gz] [ps]

A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation: Andreas Ziehe, Pavel Laskov, Guido Nolte, Klaus-Robert Müller; 5(Jul):777--800, 2004.
[abs][pdf] [ps.gz] [ps]

Feature Discovery in Non-Metric Pairwise Data: Julian Laub, Klaus-Robert Müller; 5(Jul):801--818, 2004.
[abs][pdf] [ps.gz] [ps]

Probability Product Kernels (Special Topic on Learning Theory): Tony Jebara, Risi Kondor, Andrew Howard; 5(Jul):819--844, 2004.
[abs][pdf] [ps.gz] [ps]

Feature Selection for Unsupervised Learning: Jennifer G. Dy, Carla E. Brodley; 5(Aug):845--889, 2004.
[abs][pdf] [ps.gz] [ps]

Some Dichotomy Theorems for Neural Learning Problems: Michael Schmitt; 5(Aug):891--912, 2004.
[abs][pdf] [ps.gz] [ps]

Image Categorization by Learning and Reasoning with Regions: Yixin Chen, James Z. Wang; 5(Aug):913--939, 2004.
[abs][pdf] [ps.gz] [ps]

Boosting as a Regularized Path to a Maximum Margin Classifier: Saharon Rosset, Ji Zhu, Trevor Hastie; 5(Aug):941--973, 2004.
[abs][pdf] [ps.gz] [ps]

Probability Estimates for Multi-class Classification by Pairwise Coupling: Ting-Fan Wu, Chih-Jen Lin, Ruby C. Weng; 5(Aug):975--1005, 2004.
[abs][pdf] [ps.gz] [ps]

On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition: Andreas Christmann, Ingo Steinwart; 5(Aug):1007--1034, 2004.
[abs][pdf] [ps.gz] [ps]

Rational Kernels: Theory and Algorithms (Special Topic on Learning Theory): Corinna Cortes, Patrick Haffner, Mehryar Mohri; 5(Aug):1035--1062, 2004.
[abs][pdf] [ps.gz] [ps]

Reinforcement Learning with Factored States and Actions: Brian Sallans, Geoffrey E. Hinton; 5(Aug):1063--1088, 2004.
[abs][pdf] [ps.gz] [ps]

No Unbiased Estimator of the Variance of K-Fold Cross-Validation: Yoshua Bengio, Yves Grandvalet; 5(Sep):1089--1105, 2004.
[abs][pdf] [ps.gz] [ps]

Selective Rademacher Penalization and Reduced Error Pruning of Decision Trees: Matti Kääriäinen, Tuomo Malinen, Tapio Elomaa; 5(Sep):1107--1126, 2004.
[abs][pdf]

Knowledge-Based Kernel Approximation: Olvi L. Mangasarian, Jude W. Shavlik, Edward W. Wild; 5(Sep):1127--1141, 2004.
[abs][pdf]

Support Vector Machine Soft Margin Classifiers: Error Analysis: Di-Rong Chen, Qiang Wu, Yiming Ying, Ding-Xuan Zhou; 5(Sep):1143--1175, 2004.
[abs][pdf]

Model Averaging for Prediction with Discrete Bayesian Networks: Denver Dash, Gregory F. Cooper; 5(Sep):1177--1203, 2004.
[abs][pdf]

Efficient Feature Selection via Analysis of Relevance and Redundancy: Lei Yu, Huan Liu; 5(Oct):1205--1224, 2004.
[abs][pdf]

Statistical Analysis of Some Multi-Category Large Margin Classification Methods: Tong Zhang; 5(Oct):1225--1251, 2004.
[abs][pdf]

The Minimum Error Minimax Probability Machine: Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, Laiwan Chan; 5(Oct):1253--1286, 2004.
[abs][pdf]

Large-Sample Learning of Bayesian Networks is NP-Hard: David Maxwell Chickering, David Heckerman, Christopher Meek; 5(Oct):1287--1330, 2004.
[abs][pdf]

Randomized Variable Elimination: David J. Stracuzzi, Paul E. Utgoff; 5(Oct):1331--1362, 2004.
[abs][pdf] [code]

Some Properties of Regularized Kernel Methods: Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Michele Piana, Alessandro Verri; 5(Oct):1363--1390, 2004.
[abs][pdf]

The Entire Regularization Path for the Support Vector Machine: Trevor Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu; 5(Oct):1391--1415, 2004.
[abs][pdf]

Second Order Cone Programming Formulations for Feature Selection: Chiranjib Bhattacharyya; 5(Nov):1417--1433, 2004.
[abs][pdf]

Fast String Kernels using Inexact Matching for Protein Sequences: Christina Leslie, Rui Kuang; 5(Nov):1435--1455, 2004.
[abs][pdf]

Non-negative Matrix Factorization with Sparseness Constraints: Patrik O. Hoyer; 5(Nov):1457--1469, 2004.
[abs][pdf]

Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning: Evan Greensmith, Peter L. Bartlett, Jonathan Baxter; 5(Nov):1471--1530, 2004.
[abs][pdf]

Fast Binary Feature Selection with Conditional Mutual Information: François Fleuret; 5(Nov):1531--1555, 2004.
[abs][pdf]

The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins: Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire; 5(Dec):1557--1595, 2004.
[abs][pdf]

JMLR Volume 6

Asymptotic Model Selection for Naive Bayesian Networks: Dmitry Rusakov, Dan Geiger; 6(Jan):1--35, 2005.
[abs][pdf]

Dimension Reduction in Text Classification with Support Vector Machines: Hyunsoo Kim, Peg Howland, Haesun Park; 6(Jan):37--53, 2005.
[abs][pdf]

Stability of Randomized Learning Algorithms: Andre Elisseeff, Theodoros Evgeniou, Massimiliano Pontil; 6(Jan):55--79, 2005.
[abs][pdf]

Learning Hidden Variable Networks: The Information Bottleneck Approach: Gal Elidan, Nir Friedman; 6(Jan):81--127, 2005.
[abs][pdf]

Diffusion Kernels on Statistical Manifolds: John Lafferty, Guy Lebanon; 6(Jan):129--163, 2005.
[abs][pdf]

Information Bottleneck for Gaussian Variables: Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss; 6(Jan):165--188, 2005.
[abs][pdf]

Multiclass Boosting for Weak Classifiers: Günther Eibl, Karl-Peter Pfeiffer; 6(Feb):189--210, 2005.
[abs][pdf]

A Classification Framework for Anomaly Detection: Ingo Steinwart, Don Hush, Clint Scovel; 6(Feb):211--232, 2005.
[abs][pdf]

Denoising Source Separation: Jaakko Särelä, Harri Valpola; 6(Mar):233--272, 2005.
[abs][pdf]

Tutorial on Practical Prediction Theory for Classification: John Langford; 6(Mar):273--306, 2005.
[abs][pdf]

Generalization Bounds and Complexities Based on Sparsity and Clustering for Convex Combinations of Functions from Random Classes: Savina Andonova Jaeger; 6(Mar):307--340, 2005.
[abs][pdf]

A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs: S. Sathiya Keerthi, Dennis DeCoste; 6(Mar):341--361, 2005.
[abs][pdf]

Core Vector Machines: Fast SVM Training on Very Large Data Sets: Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung; 6(Apr):363--392, 2005.
[abs][pdf]

Generalization Bounds for the Area Under the ROC Curve: Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth; 6(Apr):393--425, 2005.
[abs][pdf]

Learning with Decision Lists of Data-Dependent Features: Mario Marchand, Marina Sokolova; 6(Apr):427--451, 2005.
[abs][pdf]

Estimating Functions for Blind Separation When Sources Have Variance Dependencies: Motoaki Kawanabe, Klaus-Robert Müller; 6(Apr):453--482, 2005.
[abs][pdf]

Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems: Jieping Ye; 6(Apr):483--502, 2005.
[abs][pdf]

Tree-Based Batch Mode Reinforcement Learning: Damien Ernst, Pierre Geurts, Louis Wehenkel; 6(Apr):503--556, 2005.
[abs][pdf]

Learning Module Networks: Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman; 6(Apr):557--588, 2005.
[abs][pdf]

Active Learning to Recognize Multiple Types of Plankton: Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott Samson, Andrew Remsen, Thomas Hopkins; 6(Apr):589--613, 2005.
[abs][pdf]

Learning Multiple Tasks with Kernel Methods: Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil; 6(Apr):615--637, 2005.
[abs][pdf]

Adaptive Online Prediction by Following the Perturbed Leader: Marcus Hutter, Jan Poland; 6(Apr):639--660, 2005.
[abs][pdf]

Variational Message Passing: John Winn, Christopher M. Bishop; 6(Apr):661--694, 2005.
[abs][pdf]

Estimation of Non-Normalized Statistical Models by Score Matching: Aapo Hyvärinen; 6(Apr):695--709, 2005.
[abs][pdf]

Smooth ε-Insensitive Regression by Loss Symmetrization: Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer; 6(May):711--741, 2005.
[abs][pdf]

Quasi-Geodesic Neural Learning Algorithms Over the Orthogonal Group: A Tutorial: Simone Fiori; 6(May):743--781, 2005.
[abs][pdf]

Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application: Joseph F. Murray, Gordon F. Hughes, Kenneth Kreutz-Delgado; 6(May):783--816, 2005.
[abs][pdf]

Multiclass Classification with Multi-Prototype Support Vector Machines: Fabio Aiolli, Alessandro Sperduti; 6(May):817--850, 2005.
[abs][pdf]

Prioritization Methods for Accelerating MDP Solvers: David Wingate, Kevin D. Seppi; 6(May):851--881, 2005.
[abs][pdf]

Learning from Examples as an Inverse Problem: Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Umberto De Giovannini, Francesca Odone; 6(May):883--904, 2005.
[abs][pdf]

Loopy Belief Propagation: Convergence and Effects of Message Errors: Alexander T. Ihler, John W. Fisher III, Alan S. Willsky; 6(May):905--936, 2005.
[abs][pdf]

Learning a Mahalanobis Metric from Equivalence Constraints: Aharon Bar-Hillel, Tomer Hertz, Noam Shental, Daphna Weinshall; 6(Jun):937--965, 2005.
[abs][pdf]

Algorithmic Stability and Meta-Learning: Andreas Maurer; 6(Jun):967--994, 2005.
[abs][pdf]

Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection: Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth; 6(Jun):995--1018, 2005.
[abs][pdf]

Gaussian Processes for Ordinal Regression: Wei Chu, Zoubin Ghahramani; 6(Jul):1019--1041, 2005.
[abs][pdf]

Learning the Kernel with Hyperkernels (Kernel Machines Section): Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson; 6(Jul):1043--1071, 2005.
[abs][pdf]

A Generalization Error for Q-Learning: Susan A. Murphy; 6(Jul):1073--1097, 2005.
[abs][pdf]

Learning the Kernel Function via Regularization: Charles A. Micchelli, Massimiliano Pontil; 6(Jul):1099--1125, 2005.
[abs][pdf]

Analysis of Variance of Cross-Validation Estimators of the Generalization Error: Marianthi Markatou, Hong Tian, Shameek Biswas, George Hripcsak; 6(Jul):1127--1168, 2005.
[abs][pdf]

Semigroup Kernels on Measures: Marco Cuturi, Kenji Fukumizu, Jean-Philippe Vert; 6(Jul):1169--1198, 2005.
[abs][pdf]

Separating a Real-Life Nonlinear Image Mixture: Luís B. Almeida; 6(Jul):1199--1229, 2005.
[abs][pdf]

Concentration Bounds for Unigram Language Models: Evgeny Drukh, Yishay Mansour; 6(Aug):1231--1264, 2005.
[abs][pdf]

An MDP-Based Recommender System: Guy Shani, David Heckerman, Ronen I. Brafman; 6(Sep):1265--1295, 2005.
[abs][pdf]

Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions: Peter Binev, Albert Cohen, Wolfgang Dahmen, Ronald DeVore, Vladimir Temlyakov; 6(Sep):1297--1321, 2005.
[abs][pdf]

Efficient Computation of Gapped Substring Kernels on Large Alphabets: Juho Rousu, John Shawe-Taylor; 6(Sep):1323--1344, 2005.
[abs][pdf]

Clustering on the Unit Hypersphere using von Mises-Fisher Distributions: Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra; 6(Sep):1345--1382, 2005.
[abs][pdf]

Inner Product Spaces for Bayesian Networks: Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon; 6(Sep):1383--1403, 2005.
[abs][pdf]

Maximum Margin Algorithms with Boolean Kernels: Roni Khardon, Rocco A. Servedio; 6(Sep):1405--1429, 2005.
[abs][pdf]

A Bayes Optimal Approach for Partitioning the Values of Categorical Attributes: Marc Boullé; 6(Sep):1431--1452, 2005.
[abs][pdf]

Large Margin Methods for Structured and Interdependent Output Variables: Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun; 6(Sep):1453--1484, 2005.
[abs][pdf]

Frames, Reproducing Kernels, Regularization and Learning: Alain Rakotomamonjy, Stéphane Canu; 6(Sep):1485--1515, 2005.
[abs][pdf]

Local Propagation in Conditional Gaussian Bayesian Networks: Robert G. Cowell; 6(Sep):1517--1550, 2005.
[abs][pdf]

A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior: Hal Daumé III, Daniel Marcu; 6(Sep):1551--1577, 2005.
[abs][pdf]

Fast Kernel Classifiers with Online and Active Learning: Antoine Bordes, Seyda Ertekin, Jason Weston, Léon Bottou; 6(Sep):1579--1619, 2005.
[abs][pdf]

Managing Diversity in Regression Ensembles: Gavin Brown, Jeremy L. Wyatt, Peter Tiňo; 6(Sep):1621--1650, 2005.
[abs][pdf]

Active Coevolutionary Learning of Deterministic Finite Automata: Josh Bongard, Hod Lipson; 6(Oct):1651--1678, 2005.
[abs][pdf]

Assessing Approximate Inference for Binary Gaussian Process Classification: Malte Kuss, Carl Edward Rasmussen; 6(Oct):1679--1704, 2005.
[abs][pdf]

Clustering with Bregman Divergences: Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, Joydeep Ghosh; 6(Oct):1705--1749, 2005.
[abs][pdf]

Combining Information Extraction Systems Using Voting and Stacked Generalization: Georgios Sigletos, Georgios Paliouras, Constantine D. Spyropoulos, Michalis Hatzopoulos; 6(Nov):1751--1782, 2005.
[abs][pdf]

Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models: Neil Lawrence; 6(Nov):1783--1816, 2005.
[abs][pdf]

A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data: Rie Kubota Ando, Tong Zhang; 6(Nov):1817--1853, 2005.
[abs][pdf]

Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach: Lior Wolf, Amnon Shashua; 6(Nov):1855--1887, 2005.
[abs][pdf]

Working Set Selection Using Second Order Information for Training Support Vector Machines: Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin; 6(Dec):1889--1918, 2005.
[abs][pdf]

New Horn Revision Algorithms: Judy Goldsmith, Robert H. Sloan; 6(Dec):1919--1938, 2005.
[abs][pdf]

A Unifying View of Sparse Approximate Gaussian Process Regression: Joaquin Quiñonero-Candela, Carl Edward Rasmussen; 6(Dec):1939--1959, 2005.
[abs][pdf]

What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks: Weng-Keen Wong, Andrew Moore, Gregory Cooper, Michael Wagner; 6(Dec):1961--1998, 2005.
[abs][pdf]

Change Point Problems in Linear Dynamical Systems: Onno Zoeter, Tom Heskes; 6(Dec):1999--2026, 2005.
[abs][pdf]

Asymptotics in Empirical Risk Minimization: Leila Mohammadi, Sara van de Geer; 6(Dec):2027--2047, 2005.
[abs][pdf]

Convergence Theorems for Generalized Alternating Minimization Procedures: Asela Gunawardana, William Byrne; 6(Dec):2049--2073, 2005.
[abs][pdf]

Kernel Methods for Measuring Independence: Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, Bernhard Schölkopf; 6(Dec):2075--2129, 2005.
[abs][pdf]

Efficient Margin Maximizing with Boosting: Gunnar Rätsch, Manfred K. Warmuth; 6(Dec):2131--2152, 2005.
[abs][pdf]

On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning: Petros Drineas, Michael W. Mahoney; 6(Dec):2153--2175, 2005.
[abs][pdf]

Expectation Consistent Approximate Inference: Manfred Opper, Ole Winther; 6(Dec):2177--2204, 2005.
[abs][pdf]

JMLR Volume 7

Statistical Comparisons of Classifiers over Multiple Data Sets: Janez Demšar; 7(Jan):1--30, 2006.
[abs][pdf]

Incremental Algorithms for Hierarchical Classification: Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni; 7(Jan):31--54, 2006.
[abs][pdf]

On the Complexity of Learning Lexicographic Strategies: Michael Schmitt, Laura Martignon; 7(Jan):55--83, 2006.
[abs][pdf]

Generalized Bradley-Terry Models and Multi-Class Probability Estimates: Tzu-Kuo Huang, Ruby C. Weng, Chih-Jen Lin; 7(Jan):85--115, 2006.
[abs][pdf]

Bounds for Linear Multi-Task Learning: Andreas Maurer; 7(Jan):117--139, 2006.
[abs][pdf]

Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error: Masashi Sugiyama; 7(Jan):141--166, 2006.
[abs][pdf]

MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals: Dana Pe'er, Amos Tanay, Aviv Regev; 7(Feb):167--189, 2006.
[abs][pdf]

Learning the Structure of Linear Latent Variable Models: Ricardo Silva, Richard Scheine, Clark Glymour, Peter Spirtes; 7(Feb):191--246, 2006.
[abs][pdf]

In Search of Non-Gaussian Components of a High-Dimensional Distribution: Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiyama, Vladimir Spokoiny, Klaus-Robert Müller; 7(Feb):247--282, 2006.
[abs][pdf]

Some Discriminant-Based PAC Algorithms: Paul W. Goldberg; 7(Feb):283--306, 2006.
[abs][pdf]

Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting (Special Topic on Inductive Programming): Andrea Passerini, Paolo Frasconi, Luc De Raedt; 7(Feb):307--342, 2006.
[abs][pdf]

Using Machine Learning to Guide Architecture Simulation: Greg Hamerly, Erez Perelman, Jeremy Lau, Brad Calder, Timothy Sherwood; 7(Feb):343--378, 2006.
[abs][pdf]

Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition: Ron Begleiter, Ran El-Yaniv; 7(Feb):379--411, 2006.
[abs][pdf]

Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation: Rémi Munos; 7(Feb):413--427, 2006.
[abs][pdf]

Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach (Special Topic on Inductive Programming): Emanuel Kitzelmann, Ute Schmid; 7(Feb):429--454, 2006.
[abs][pdf]

Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis: Tonatiuh Peña Centeno, Neil D. Lawrence; 7(Feb):455--491, 2006.
[abs][pdf]

Learning Recursive Control Programs from Problem Solving (Special Topic on Inductive Programming): Pat Langley, Dongkyu Choi; 7(Mar):493--518, 2006.
[abs][pdf]

Learning Coordinate Covariances via Gradients: Sayan Mukherjee, Ding-Xuan Zhou; 7(Mar):519--549, 2006.
[abs][pdf]

Online Passive-Aggressive Algorithms: Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer; 7(Mar):551--585, 2006.
[abs][pdf]

Toward Attribute Efficient Learning of Decision Lists and Parities: Adam R. Klivans, Rocco A. Servedio; 7(Apr):587--602, 2006.
[abs][pdf]

A Direct Method for Building Sparse Kernel Learning Algorithms: Mingrui Wu, Bernhard Schölkopf, Gökhan Bakır; 7(Apr):603--624, 2006.
[abs][pdf]

Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation: Kazuho Watanabe, Sumio Watanabe; 7(Apr):625--644, 2006.
[abs][pdf]

Pattern Recognition for Conditionally Independent Data: Daniil Ryabko; 7(Apr):645--664, 2006.
[abs][pdf]

Learning Minimum Volume Sets: Clayton D. Scott, Robert D. Nowak; 7(Apr):665--704, 2006.
[abs][pdf]

Some Theory for Generalized Boosting Algorithms: Peter J. Bickel, Ya'acov Ritov, Alon Zakai; 7(May):705--732, 2006.
[abs][pdf]

QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines: Don Hush, Patrick Kelly, Clint Scovel, Ingo Steinwart; 7(May):733--769, 2006.
[abs][pdf]

Policy Gradient in Continuous Time: Rémi Munos; 7(May):771--791, 2006.
[abs][pdf]

Learning Image Components for Object Recognition: Michael W. Spratling; 7(May):793--815, 2006.
[abs][pdf]

Consistency and Convergence Rates of One-Class SVMs and Related Algorithms: Régis Vert, Jean-Philippe Vert; 7(May):817--854, 2006.
[abs][pdf]

Infinite-σ Limits For Tikhonov Regularization: Ross A. Lippert, Ryan M. Rifkin; 7(May):855--876, 2006.
[abs][pdf]

Evolutionary Function Approximation for Reinforcement Learning: Shimon Whiteson, Peter Stone; 7(May):877--917, 2006.
[abs][pdf]

Rearrangement Clustering: Pitfalls, Remedies, and Applications: Sharlee Climer, Weixiong Zhang; 7(Jun):919--943, 2006.
[abs][pdf]

Segmental Hidden Markov Models with Random Effects for Waveform Modeling: Seyoung Kim, Padhraic Smyth; 7(Jun):945--969, 2006.
[abs][pdf]

Lower Bounds and Aggregation in Density Estimation: Guillaume Lecué; 7(Jun):971--981, 2006.
[abs][pdf]

Quantile Regression Forests: Nicolai Meinshausen; 7(Jun):983--999, 2006.
[abs][pdf]

Sparse Boosting: Peter Bühlmann, Bin Yu; 7(Jun):1001--1024, 2006.
[abs][pdf]

One-Class Novelty Detection for Seizure Analysis from Intracranial EEG: Andrew B. Gardner, Abba M. Krieger, George Vachtsevanos, Brian Litt; 7(Jun):1025--1044, 2006.
[abs][pdf]

A Graphical Representation of Equivalence Classes of AMP Chain Graphs: Alberto Roverato, Milan Studený; 7(Jun):1045--1078, 2006.
[abs][pdf]

Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems: Eyal Even-Dar, Shie Mannor, Yishay Mansour; 7(Jun):1079--1105, 2006.
[abs][pdf]

Step Size Adaptation in Reproducing Kernel Hilbert Space: S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex J. Smola; 7(Jun):1107--1133, 2006.
[abs][pdf]

New Algorithms for Efficient High-Dimensional Nonparametric Classification: Ting Liu, Andrew W. Moore, Alexander Gray; 7(Jun):1135--1158, 2006.
[abs][pdf]

A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis: Enrique Castillo, Bertha Guijarro-Berdiñas, Oscar Fontenla-Romero, Amparo Alonso-Betanzos; 7(Jul):1159--1182, 2006.
[abs][pdf]

Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis: Jieping Ye, Tao Xiong; 7(Jul):1183--1204, 2006.
[abs][pdf]

Worst-Case Analysis of Selective Sampling for Linear Classification: Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni; 7(Jul):1205--1230, 2006.
[abs][pdf]

Nonparametric Quantile Estimation: Ichiro Takeuchi, Quoc V. Le, Timothy D. Sears, Alexander J. Smola; 7(Jul):1231--1264, 2006.
[abs][pdf]

The Interplay of Optimization and Machine Learning Research (Special Topic on Machine Learning and Optimization): Kristin P. Bennett, Emilio Parrado-Hernández; 7(Jul):1265--1281, 2006.
[abs][pdf]

Second Order Cone Programming Approaches for Handling Missing and Uncertain Data (Special Topic on Machine Learning and Optimization): Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola; 7(Jul):1283--1314, 2006.
[abs][pdf]

Ensemble Pruning Via Semi-definite Programming (Special Topic on Machine Learning and Optimization): Yi Zhang, Samuel Burer, W. Nick Street; 7(Jul):1315--1338, 2006.
[abs][pdf]

Linear Programs for Hypotheses Selection in Probabilistic Inference Models (Special Topic on Machine Learning and Optimization): Anders Bergkvist, Peter Damaschke, Marcel Lüthi; 7(Jul):1339--1355, 2006.
[abs][pdf]

Bayesian Network Learning with Parameter Constraints (Special Topic on Machine Learning and Optimization): Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat Rao; 7(Jul):1357--1383, 2006.
[abs][pdf]

Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming (Special Topic on Machine Learning and Optimization): Matthias Heiler, Christoph Schnörr; 7(Jul):1385--1407, 2006.
[abs][pdf]

Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems (Special Topic on Machine Learning and Optimization): Tijl De Bie, Nello Cristianini; 7(Jul):1409--1436, 2006.
[abs][pdf]

Maximum-Gain Working Set Selection for SVMs (Special Topic on Machine Learning and Optimization): Tobias Glasmachers, Christian Igel; 7(Jul):1437--1466, 2006.
[abs][pdf]

Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems (Special Topic on Machine Learning and Optimization): Luca Zanni, Thomas Serafini, Gaetano Zanghirati; 7(Jul):1467--1492, 2006.
[abs][pdf]

Building Support Vector Machines with Reduced Classifier Complexity (Special Topic on Machine Learning and Optimization): S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste; 7(Jul):1493--1515, 2006.
[abs][pdf]

Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization (Special Topic on Machine Learning and Optimization): Olvi L. Mangasarian; 7(Jul):1517--1530, 2006.
[abs][pdf]

Large Scale Multiple Kernel Learning (Special Topic on Machine Learning and Optimization): Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, Bernhard Schölkopf; 7(Jul):1531--1565, 2006.
[abs][pdf]

Efficient Learning of Label Ranking by Soft Projections onto Polyhedra (Special Topic on Machine Learning and Optimization): Shai Shalev-Shwartz, Yoram Singer; 7(Jul):1567--1599, 2006.
[abs][pdf] [code]

Kernel-Based Learning of Hierarchical Multilabel Classification Models (Special Topic on Machine Learning and Optimization): Juho Rousu, Craig Saunders, Sandor Szedmak, John Shawe-Taylor; 7(Jul):1601--1626, 2006.
[abs][pdf]

Structured Prediction, Dual Extragradient and Bregman Projections (Special Topic on Machine Learning and Optimization): Ben Taskar, Simon Lacoste-Julien, Michael I. Jordan; 7(Jul):1627--1653, 2006.
[abs][pdf]

Active Learning with Feedback on Features and Instances: Hema Raghavan, Omid Madani, Rosie Jones; 7(Aug):1655--1686, 2006.
[abs][pdf]

Large Scale Transductive SVMs: Ronan Collobert, Fabian Sinz, Jason Weston, Léon Bottou; 7(Aug):1687--1712, 2006.
[abs][pdf]

Considering Cost Asymmetry in Learning Classifiers: Francis R. Bach, David Heckerman, Eric Horvitz; 7(Aug):1713--1741, 2006.
[abs][pdf]

Learning Factor Graphs in Polynomial Time and Sample Complexity: Pieter Abbeel, Daphne Koller, Andrew Y. Ng; 7(Aug):1743--1788, 2006.
[abs][pdf]

Collaborative Multiagent Reinforcement Learning by Payoff Propagation: Jelle R. Kok, Nikos Vlassis; 7(Sep):1789--1828, 2006.
[abs][pdf]

Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting: Martin J. Wainwright; 7(Sep):1829--1859, 2006.
[abs][pdf]

Streamwise Feature Selection: Jing Zhou, Dean P. Foster, Robert A. Stine, Lyle H. Ungar; 7(Sep):1861--1885, 2006.
[abs][pdf]

Linear Programming Relaxations and Belief Propagation -- An Empirical Study (Special Topic on Machine Learning and Optimization): Chen Yanover, Talya Meltzer, Yair Weiss; 7(Sep):1887--1907, 2006.
[abs][pdf] [data]

Incremental Support Vector Learning: Analysis, Implementation and Applications (Special Topic on Machine Learning and Optimization): Pavel Laskov, Christian Gehl, Stefan Krüger, Klaus-Robert Müller; 7(Sep):1909--1936, 2006.
[abs][pdf]

A Simulation-Based Algorithm for Ergodic Control of Markov Chains Conditioned on Rare Events: Shalabh Bhatnagar, Vivek S. Borkar, Madhukar Akarapu; 7(Oct):1937--1962, 2006.
[abs][pdf]

Learning Spectral Clustering, With Application To Speech Separation: Francis R. Bach, Michael I. Jordan; 7(Oct):1963--2001, 2006.
[abs][pdf]

A Linear Non-Gaussian Acyclic Model for Causal Discovery: Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti Kerminen; 7(Oct):2003--2030, 2006.
[abs][pdf]

Walk-Sums and Belief Propagation in Gaussian Graphical Models: Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky; 7(Oct):2031--2064, 2006.
[abs][pdf]

Distance Patterns in Structural Similarity: Thomas Kämpke; 7(Oct):2065--2086, 2006.
[abs][pdf]

A Hierarchy of Support Vector Machines for Pattern Detection: Hichem Sahbi, Donald Geman; 7(Oct):2087--2123, 2006.
[abs][pdf]

Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies: Fu Chang, Chin-Chin Lin, Chi-Jen Lu; 7(Oct):2125--2148, 2006.
[abs][pdf]

A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests: Luis M. de Campos; 7(Oct):2149--2187, 2006.
[abs][pdf]

Noisy-OR Component Analysis and its Application to Link Analysis: Tomáš Šingliar, Miloš Hauskrecht; 7(Oct):2189--2213, 2006.
[abs][pdf]

Learning a Hidden Hypergraph: Dana Angluin, Jiang Chen; 7(Oct):2215--2236, 2006.
[abs][pdf]

An Efficient Implementation of an Active Set Method for SVMs (Special Topic on Machine Learning and Optimization): Katya Scheinberg; 7(Oct):2237--2257, 2006.
[abs][pdf]

Causal Graph Based Decomposition of Factored MDPs: Anders Jonsson, Andrew Barto; 7(Nov):2259--2301, 2006.
[abs][pdf]

Accurate Error Bounds for the Eigenvalues of the Kernel Matrix: Mikio L. Braun; 7(Nov):2303--2328, 2006.
[abs][pdf]

Point-Based Value Iteration for Continuous POMDPs: Josep M. Porta, Nikos Vlassis, Matthijs T.J. Spaan, Pascal Poupart; 7(Nov):2329--2367, 2006.
[abs][pdf]

Learning Parts-Based Representations of Data: David A. Ross, Richard S. Zemel; 7(Nov):2369--2397, 2006.
[abs][pdf]

Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples: Mikhail Belkin, Partha Niyogi, Vikas Sindhwani; 7(Nov):2399--2434, 2006.
[abs][pdf]

Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss: Di-Rong Chen, Tao Sun; 7(Nov):2435--2447, 2006.
[abs][pdf]

Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation: Magnus Ekdahl, Timo Koski; 7(Nov):2449--2480, 2006.
[abs][pdf]

Estimation of Gradients and Coordinate Covariation in Classification: Sayan Mukherjee, Qiang Wu; 7(Nov):2481--2514, 2006.
[abs][pdf]

Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems: David Barber; 7(Nov):2515--2540, 2006.
[abs][pdf]

On Model Selection Consistency of Lasso: Peng Zhao, Bin Yu; 7(Nov):2541--2563, 2006.
[abs][pdf]

Stability Properties of Empirical Risk Minimization over Donsker Classes: Andrea Caponnetto, Alexander Rakhlin; 7(Dec):2565--2583, 2006.
[abs][pdf]

Linear State-Space Models for Blind Source Separation: Rasmus Kongsgaard Olsson, Lars Kai Hansen; 7(Dec):2585--2602, 2006.
[abs][pdf]

On Representing and Generating Kernels by Fuzzy Equivalence Relations: Bernhard Moser; 7(Dec):2603--2620, 2006.
[abs][pdf]

A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n: Robert Castelo, Alberto Roverato; 7(Dec):2621--2650, 2006.
[abs][pdf]

Universal Kernels: Charles A. Micchelli, Yuesheng Xu, Haizhang Zhang; 7(Dec):2651--2667, 2006.
[abs][pdf]

Machine Learning for Computer Security (Special Topic on Machine Learning for Computer Security): Philip K. Chan, Richard P. Lippmann; 7(Dec):2669--2672, 2006.
[abs][pdf]

Spam Filtering Using Statistical Data Compression Models (Special Topic on Machine Learning for Computer Security): Andrej Bratko, Gordon V. Cormack, Bogdan Filipič, Thomas R. Lynam, Blaž Zupan; 7(Dec):2673--2698, 2006.
[abs][pdf]

Spam Filtering Based On The Analysis Of Text Information Embedded Into Images (Special Topic on Machine Learning for Computer Security): Giorgio Fumera, Ignazio Pillai, Fabio Roli; 7(Dec):2699--2720, 2006.
[abs][pdf]

Learning to Detect and Classify Malicious Executables in the Wild (Special Topic on Machine Learning for Computer Security): J. Zico Kolter, Marcus A. Maloof; 7(Dec):2721--2744, 2006.
[abs][pdf]

On Inferring Application Protocol Behaviors in Encrypted Network Traffic (Special Topic on Machine Learning for Computer Security): Charles V. Wright, Fabian Monrose, Gerald M. Masson; 7(Dec):2745--2769, 2006.
[abs][pdf]

JMLR Volume 8

Nonlinear Boosting Projections for Ensemble Construction: Nicolás García-Pedrajas, César García-Osorio, Colin Fyfe; 8(Jan):1--33, 2007.
[abs][pdf]

Multi-Task Learning for Classification with Dirichlet Process Priors: Ya Xue, Xuejun Liao, Lawrence Carin, Balaji Krishnapuram; 8(Jan):35--63, 2007.
[abs][pdf]

A Unified Continuous Optimization Framework for Center-Based Clustering Methods: Marc Teboulle; 8(Jan):65--102, 2007.
[abs][pdf]

Minimax Regret Classifier for Imprecise Class Distributions: Rocío Alaiz-Rodríguez, Alicia Guerrero-Curieses, Jesús Cid-Sueiro; 8(Jan):103--130, 2007.
[abs][pdf]

Distances between Data Sets Based on Summary Statistics: Nikolaj Tatti; 8(Jan):131--154, 2007.
[abs][pdf]

Building Blocks for Variational Bayesian Learning of Latent Variable Models: Tapani Raiko, Harri Valpola, Markus Harva, Juha Karhunen; 8(Jan):155--201, 2007.
[abs][pdf]

A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians: Sanjoy Dasgupta, Leonard Schulman; 8(Feb):203--226, 2007.
[abs][pdf]

Noise Tolerant Variants of the Perceptron Algorithm: Roni Khardon, Gabriel Wachman; 8(Feb):227--248, 2007.
[abs][pdf]

Learnability of Gaussians with Flexible Variances: Yiming Ying, Ding-Xuan Zhou; 8(Feb):249--276, 2007.
[abs][pdf]

Separating Models of Learning from Correlated and Uncorrelated Data: Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andrew Wan; 8(Feb):277--290, 2007.
[abs][pdf]

Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets": Gaëlle Loosli, Stéphane Canu; 8(Feb):291--301, 2007.
[abs][pdf]

General Polynomial Time Decomposition Algorithms: Nikolas List, Hans Ulrich Simon; 8(Feb):303--321, 2007.
[abs][pdf]

Dynamics and Generalization Ability of LVQ Algorithms: Michael Biehl, Anarta Ghosh, Barbara Hammer; 8(Feb):323--360, 2007.
[abs][pdf]

Statistical Consistency of Kernel Canonical Correlation Analysis: Kenji Fukumizu, Francis R. Bach, Arthur Gretton; 8(Feb):361--383, 2007.
[abs][pdf]

Learning Equivariant Functions with Matrix Valued Kernels: Marco Reisert, Hans Burkhardt; 8(Mar):385--408, 2007.
[abs][pdf]

Boosted Classification Trees and Class Probability/Quantile Estimation: David Mease, Abraham J. Wyner, Andreas Buja; 8(Mar):409--439, 2007.
[abs][pdf]

Value Regularization and Fenchel Duality: Ryan M. Rifkin, Ross A. Lippert; 8(Mar):441--479, 2007.
[abs][pdf]

Integrating Naïve Bayes and FOIL: Niels Landwehr, Kristian Kersting, Luc De Raedt; 8(Mar):481--507, 2007.
[abs][pdf]

A Stochastic Algorithm for Feature Selection in Pattern Recognition: Sébastien Gadat, Laurent Younes; 8(Mar):509--547, 2007.
[abs][pdf]

Learning Horn Expressions with LOGAN-H: Marta Arias, Roni Khardon, Jérôme Maloberti; 8(Mar):549--587, 2007.
[abs][pdf]

Consistent Feature Selection for Pattern Recognition in Polynomial Time: Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér; 8(Mar):589--612, 2007.
[abs][pdf]

Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm: Markus Kalisch, Peter Bühlmann; 8(Mar):613--636, 2007.
[abs][pdf]

Margin Trees for High-dimensional Classification: Robert Tibshirani, Trevor Hastie; 8(Mar):637--652, 2007.
[abs][pdf]

Relational Dependency Networks: Jennifer Neville, David Jensen; 8(Mar):653--692, 2007.
[abs][pdf]

Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data: Charles Sutton, Andrew McCallum, Khashayar Rohanimanesh; 8(Mar):693--723, 2007.
[abs][pdf]

The Pyramid Match Kernel: Efficient Learning with Sets of Features: Kristen Grauman, Trevor Darrell; 8(Apr):725--760, 2007.
[abs][pdf]

Infinitely Imbalanced Logistic Regression: Art B. Owen; 8(Apr):761--773, 2007.
[abs][pdf]

Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results: Peter L. Bartlett, Ambuj Tewari; 8(Apr):775--790, 2007.
[abs][pdf]

Concave Learners for Rankboost: Ofer Melnik, Yehuda Vardi, Cun-Hui Zhang; 8(Apr):791--812, 2007.
[abs][pdf]

Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression: Shantanu Chakrabartty, Gert Cauwenberghs; 8(Apr):813--839, 2007.
[abs][pdf]

Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters: Gavin C. Cawley, Nicola L. C. Talbot; 8(Apr):841--861, 2007.
[abs][pdf]

Combining PAC-Bayesian and Generic Chaining Bounds: Jean-Yves Audibert, Olivier Bousquet; 8(Apr):863--889, 2007.
[abs][pdf]

JMLR Special Topic on Machine Learning for Computer Security

Machine Learning for Computer Security: Philip K. Chan, Richard P. Lippmann; 7(Dec):2669--2672, 2006.
[abs][pdf]

Spam Filtering Using Statistical Data Compression Models: Andrej Bratko, Bogdan Filipič, Gordon V. Cormack, Thomas R. Lynam, Blaž Zupan; 7(Dec):2673--2698, 2006.
[abs][pdf]

Spam Filtering Based On The Analysis Of Text Information Embedded Into Images: Giorgio Fumera, Ignazio Pillai, Fabio Roli; 7(Dec):2699--2720, 2006.
[abs][pdf]

Learning to Detect and Classify Malicious Executables in the Wild: J. Zico Kolter, Marcus A. Maloof; 7(Dec):2721--2744, 2006.
[abs][pdf]

On Inferring Application Protocol Behaviors in Encrypted Network Traffic: Charles V. Wright, Fabian Monrose, Gerald M. Masson; 7(Dec):2745--2769, 2006.
[abs][pdf]

JMLR Special Topic on Machine Learning and Large Scale Optimization

The Interplay of Optimization and Machine Learning Research: Kristin P. Bennett, Emilio Parrado-Hernández; 7(Jul):1265--1281, 2006.
[abs][pdf]

Second Order Cone Programming Approaches for Handling Missing and Uncertain Data: Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola; 7(Jul):1283--1314, 2006.
[abs][pdf]

Ensemble Pruning Via Semi-definite Programming: Yi Zhang, Samuel Burer, W. Nick Street; 7(Jul):1315--1338, 2006.
[abs][pdf]

Linear Programs for Hypotheses Selection in Probabilistic Inference Models: Anders Bergkvist, Peter Damaschke, Marcel Lüthi; 7(Jul):1339--1355, 2006.
[abs][pdf]

Bayesian Network Learning with Parameter Constraints: Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat Rao; 7(Jul):1357--1383, 2006.
[abs][pdf]

Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming: Matthias Heiler, Christoph Schnörr; 7(Jul):1385--1407, 2006.
[abs][pdf]

Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems: Tijl De Bie, Nello Cristianini; 7(Jul):1409--1436, 2006.
[abs][pdf]

An Efficient Implementation of an Active Set Method for SVMs: Katya Scheinberg; 7(Oct):2237--2257, 2006.
[abs][pdf]

Maximum-Gain Working Set Selection for SVMs: Tobias Glasmachers, Christian Igel; 7(Jul):1437--1466, 2006.
[abs][pdf]

Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems: Luca Zanni, Thomas Serafini, Gaetano Zanghirati; 7(Jul):1467--1492, 2006.
[abs][pdf]

Incremental Support Vector Learning: Analysis, Implementation and Applications: Pavel Laskov, Christian Gehl, Stefan Krüger, Klaus-Robert Müller; 7(Sep):1909--1936, 2006.
[abs][pdf]

Building Support Vector Machines with Reduced Classifier Complexity: S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste; 7(Jul):1493--1515, 2006.
[abs][pdf]

Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization: Olvi L. Mangasarian; 7(Jul):1517--1530, 2006.
[abs][pdf]

Linear Programming Relaxations and Belief Propagation -- An Empirical Study: Chen Yanover, Talya Meltzer, Yair Weiss; 7(Sep):1887--1907, 2006.
[abs][pdf] [data]

Large Scale Multiple Kernel Learning: Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, Bernhard Schölkopf; 7(Jul):1531--1565, 2006.
[abs][pdf]

Efficient Learning of Label Ranking by Soft Projections onto Polyhedra: Shai Shalev-Shwartz, Yoram Singer; 7(Jul):1567--1599, 2006.
[abs][pdf] [code]

Kernel-Based Learning of Hierarchical Multilabel Classification Models: Juho Rousu, Craig Saunders, Sandor Szedmak, John Shawe-Taylor; 7(Jul):1601--1626, 2006.
[abs][pdf]

Structured Prediction, Dual Extragradient and Bregman Projections: Ben Taskar, Simon Lacoste-Julien, Michael I. Jordan; 7(Jul):1627--1653, 2006.
[abs][pdf]

JMLR Special Topic on Approaches and Applications of Inductive Programming

Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting: Andrea Passerini, Paolo Frasconi, Luc De Raedt; 7(Feb):307--342, 2006.
[abs][pdf]

Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach: Emanuel Kitzelmann, Ute Schmid; 7(Feb):429--454, 2006.
[abs][pdf]

Learning Recursive Control Programs from Problem Solving: Pat Langley, Dongkyu Choi; 7(Mar):493--518, 2006.
[abs][pdf]

JMLR Special Topic on Learning Theory

A Universal Well-Calibrated Algorithm for On-line Classification: Vladimir Vovk; 5(Jun):575--604, 2004.
[abs][pdf] [ps.gz] [ps]

The Sample Complexity of Exploration in the Multi-Armed Bandit Problem: Shie Mannor, John N. Tsitsiklis; 5(Jun):623--648, 2004.
[abs][pdf] [ps.gz] [ps]

Preference Elicitation and Query Learning: Avrim Blum, Jeffrey Jackson, Tuomas Sandholm, Martin Zinkevich; 5(Jun):649--667, 2004.
[abs][pdf] [ps.gz] [ps]

Distance-Based Classification with Lipschitz Functions: Ulrike von Luxburg, Olivier Bousquet; 5(Jun):669--695, 2004.
[abs][pdf] [ps.gz] [ps]

Probability Product Kernels: Tony Jebara, Risi Kondor, Andrew Howard ; 5(Jul):819--844, 2004.
[abs][pdf] [ps.gz] [ps]

Rational Kernels: Theory and Algorithms: Corinna Cortes, Patrick Haffner, Mehryar Mohri; 5(Aug):1035--1062, 2004.
[abs][pdf] [ps.gz] [ps]

JMLR Special Issue on Independent Components Analysis

Introduction to Special Issue on Independent Components Analysis: Te-Won Lee, Jean-François Cardoso, Erkki Oja, Shun-ichi Amari; 4(Dec):1175--1176, 2003.
[abs][pdf] [ps.gz] [ps]

Dependence, Correlation and Gaussianity in Independent Component Analysis: Jean-François Cardoso; 4(Dec):1177--1203, 2003.
[abs][pdf] [ps.gz] [ps]

Beyond Independent Components: Trees and Clusters: Francis R. Bach, Michael I. Jordan; 4(Dec):1205--1233, 2003.
[abs][pdf] [ps.gz] [ps]

Energy-Based Models for Sparse Overcomplete Representations: Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton; 4(Dec):1235--1260, 2003.
[abs][pdf] [ps.gz] [ps]

Blind Source Separation via Generalized Eigenvalue Decomposition: Lucas Parra, Paul Sajda; 4(Dec):1261--1269, 2003.
[abs][pdf] [ps.gz] [ps]

ICA Using Spacings Estimates of Entropy: Erik G. Learned-Miller, John W. Fisher III; 4(Dec):1271--1295, 2003.
[abs][pdf] [ps.gz] [ps]

MISEP -- Linear and Nonlinear ICA Based on Mutual Information: Luís B. Almeida; 4(Dec):1297--1318, 2003.
[abs][pdf] [ps.gz] [ps]

Blind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation: Andreas Ziehe, Motoaki Kawanabe, Stefan Harmeling, Klaus-Robert Müller; 4(Dec):1319--1338, 2003.
[abs][pdf] [ps.gz] [ps]

A Multiscale Framework For Blind Separation of Linearly Mixed Signals: Pavel Kisilev, Michael Zibulevsky, Yehoshua Y. Zeevi; 4(Dec):1339--1363, 2003.
[abs][pdf] [ps.gz] [ps]

A Maximum Likelihood Approach to Single-channel Source Separation: Gil-Jin Jang, Te-Won Lee; 4(Dec):1365--1392, 2003.
[abs][pdf] [ps.gz] [ps]

Statistical Dynamics of On-line Independent Component Analysis: Gleb Basalyga, Magnus Rattray; 4(Dec):1393--1410, 2003.
[abs][pdf] [ps.gz] [ps]

Blind Source Recovery: A Framework in the State Space: Khurram Waheed, Fathi M. Salem; 4(Dec):1411--1446, 2003.
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Overlearning in Marginal Distribution-Based ICA: Analysis and Solutions: Jaakko Särelä, Ricardo Vigário; 4(Dec):1447--1469, 2003.
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ICA for Watermarking Digital Images: Stéphane Bounkong, Borémi Toch, David Saad, David Lowe; 4(Dec):1471--1498, 2003.
[abs][pdf] [ps.gz] [ps]

A Generative Model for Separating Illumination and Reflectance from Images: Inna Stainvas, David Lowe; 4(Dec):1499--1519, 2003.
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JMLR Special Issue on Learning Theory

Introduction to the Special Issue on Learning Theory: Ralf Herbrich, Thore Graepel; 4(Oct):755--757, 2003.
[abs][pdf] [ps.gz] [ps]

On the Performance of Kernel Classes: Shahar Mendelson; 4(Oct):759--771, 2003.
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Path Kernels and Multiplicative Updates: Eiji Takimoto, Manfred K. Warmuth; 4(Oct):773--818, 2003.
[abs][pdf] [ps.gz] [ps]

Tracking Linear-threshold Concepts with Winnow: Chris Mesterharm; 4(Oct):819--838, 2003.
[abs][pdf] [ps.gz] [ps]

Generalization Error Bounds for Bayesian Mixture Algorithms: Ron Meir, Tong Zhang; 4(Oct):839--860, 2003.
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On the Rate of Convergence of Regularized Boosting Classifiers: Gilles Blanchard, G´bor Lugosi, Nicolas Vayatis; 4(Oct):861--894, 2003.
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Concentration Inequalities for the Missing Mass and for Histogram Rule Error: David McAllester, Luis Ortiz; 4(Oct):895--911, 2003.
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JMLR Special Issue on Inductive Logic Programming

Introduction to the Special Issue on Inductive Logic Programming: James Cussens, Alan M. Frisch; 4(Aug):413--414, 2003.
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ILP: A Short Look Back and a Longer Look Forward: David Page, Ashwin Srinivasan; 4(Aug):415--430, 2003.
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Relational Learning as Search in a Critical Region: Marco Botta, Attilio Giordana, Lorenza Saitta, Michèle Sebag; 4(Aug):431--463, 2003.
[abs][pdf] [ps.gz] [ps]

Query Transformations for Improving the Efficiency of ILP Systems: Vítor Santos Costa, Ashwin Srinivasan Rui Camacho, Hendrik Blockeel, Bart Demoen, Gerda Janssens, Jan Struyf, Henk Vandecasteele, Wim Van Laer; 4(Aug):465--491, 2003.
[abs][pdf] [ps.gz] [ps]

Learning Semantic Lexicons from a Part-of-Speech and Semantically Tagged Corpus Using Inductive Logic Programming: Vincent Claveau, Pascale Sébillot, Cécile Fabre, Pierrette Bouillon; 4(Aug):493--525, 2003.
[abs][pdf] [ps.gz] [ps]

JMLR Special Issue on the Fusion of Domain Knowledge with Data for Decision Support

Introduction to the Special Issue on the Fusion of Domain Knowledge with Data for Decision Support: Richard Dybowski, Kathryn B. Laskey, James W. Myers, Simon Parsons; 4(Jul):293--294, 2003.
[abs][pdf] [ps.gz] [ps]

Combining Knowledge from Different Sources in Causal Probabilistic Models: Marek J. Druzdzel, Francisco J. Díez; 4(Jul):295--316, 2003.
[abs][pdf] [ps.gz] [ps]

Preference Elicitation via Theory Refinement: Peter Haddawy, Vu Ha, Angelo Restificar, Benjamin Geisler, John Miyamoto; 4(Jul):317--337, 2003.
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Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains: Helge Langseth, Thomas D. Nielsen; 4(Jul):339--368, 2003.
[abs][pdf] [ps.gz] [ps]

An Empirical Study of the Use of Relevance Information in Inductive Logic Programming: Ashwin Srinivasan, Ross D. King, Michael E. Bain; 4(Jul):369--383, 2003.
[abs][pdf] [ps.gz] [ps]
revision: Aug 2003
[abs] [pdf] [ps.gz] [ps]

JMLR Special Issue on Variable and Feature Selection

An Introduction to Variable and Feature Selection (Kernel Machines Section): Isabelle Guyon, André Elisseeff; 3(Mar):1157--1182, 2003.
[abs][pdf] [ps.gz] [ps]

Distributional Word Clusters vs. Words for Text Categorization (Kernel Machines Section): Ron Bekkerman, Ran El-Yaniv, Naftali Tishby, Yoad Winter; 3(Mar):1183--1208, 2003.
[abs][pdf] [ps.gz] [ps] [data]

Extensions to Metric Based Model Selection: Yoshua Bengio, Nicolas Chapados; 3(Mar):1209--1227, 2003.
[abs][pdf] [ps.gz] [ps]

Dimensionality Reduction via Sparse Support Vector Machines (Kernel Machines Section): Jinbo Bi, Kristin Bennett, Mark Embrechts, Curt Breneman, Minghu Song; 3(Mar):1229--1243, 2003.
[abs][pdf] [ps.gz] [ps] [data]

Benefitting from the Variables that Variable Selection Discards: Rich Caruana, Virginia R. de Sa; 3(Mar):1245--1264, 2003.
[abs][pdf] [ps.gz] [ps]

A Divisive Information Theoretic Feature Clustering Algorithm for Text Classification (Kernel Machines Section): Inderjit S. Dhillon, Subramanyam Mallela, Rahul Kumar; 3(Mar):1265--1287, 2003.
[abs][pdf] [ps.gz] [ps]

An Extensive Empirical Study of Feature Selection Metrics for Text Classification: George Forman; 3(Mar):1289--1305, 2003.
[abs][pdf] [ps.gz] [ps] [data]

Sufficient Dimensionality Reduction (Kernel Machines Section): Amir Globerson, Naftali Tishby; 3(Mar):1307--1331, 2003.
[abs][pdf] [ps.gz] [ps]

Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space (Kernel Machines Section): Simon Perkins, Kevin Lacker, James Theiler; 3(Mar):1333--1356, 2003.
[abs][pdf] [ps.gz] [ps] [data]

Variable Selection Using SVM based Criteria (Kernel Machines Section): Alain Rakotomamonjy; 3(Mar):1357--1370, 2003.
[abs][pdf] [ps.gz] [ps] [data]

Overfitting in Making Comparisons Between Variable Selection Methods: Juha Reunanen; 3(Mar):1371--1382, 2003.
[abs][pdf] [ps.gz] [ps]

MLPs (Mono Layer Polynomials and Multi Layer Perceptrons) for Nonlinear Modeling: Isabelle Rivals, Léon Personnaz; 3(Mar):1383--1398, 2003.
[abs][pdf] [ps.gz] [ps] [data]

Ranking a Random Feature for Variable and Feature Selection: Hervé Stoppiglia, Gérard Dreyfus, Rémi Dubois, Yacine Oussar; 3(Mar):1399--1414, 2003.
[abs][pdf] [ps.gz] [ps]

Feature Extraction by Non Parametric Mutual Information Maximization (Kernel Machines Section): Kari Torkkola; 3(Mar):1415--1438, 2003.
[abs][pdf] [ps.gz] [ps] [demo]

Use of the Zero Norm with Linear Models and Kernel Methods (Kernel Machines Section): Jason Weston, André Elisseeff, Bernhard Schölkopf, Mike Tipping; 3(Mar):1439--1461, 2003.
[abs][pdf] [ps.gz] [ps] [data]

JMLR Special Issue on Machine Learning Methods for Text and Images

Introduction to the Special Issue on Machine Learning Methods for Text and Images: Jaz Kandola, Thomas Hofmann, Tomaso Poggio, John Shawe Taylor; 3(Feb):1023--1024, 2003.
[abs][pdf] [ps.gz] [ps]

A Family of Additive Online Algorithms for Category Ranking: Koby Crammer, Yoram Singer; 3(Feb):1025--1058, 2003.
[abs][pdf] [ps.gz] [ps]

Word Sequence Kernels: Nicola Cancedda, Eric Gaussier, Cyril Goutte, Jean Michel Renders; 3(Feb):1059--1082, 2003.
[abs][pdf] [ps.gz] [ps]

Kernel Methods for Relation Extraction: Dmitry Zelenko, Chinatsu Aone, Anthony Richardella; 3(Feb):1083--1106, 2003.
[abs][pdf] [ps.gz] [ps]

Matching Words and Pictures: Kobus Barnard, Pinar Duygulu, David Forsyth, Nando de Freitas, David M. Blei, Michael I. Jordan; 3(Feb):1107--1135, 2003.
[abs][pdf] [ps.gz] [ps][data]

A Neural Probabilistic Language Model: Yoshua Bengio, Réjean Ducharme, Pascal Vincent, Christian Jauvin; 3(Feb):1137--1155, 2003.
[abs][pdf] [ps.gz] [ps]

JMLR Special Issue on the Eighteenth International Conference on Machine Learning (ICML 2001)

Special Issue on the Eighteenth International Conference on Machine Learning (ICML2001): Carla E. Brodley, Andrea P. Danyluk; 3(Dec):619--620, 2002.
[abs][pdf] [ps.gz] [ps]

Efficient Algorithms for Decision Tree Cross-validation: Hendrik Blockeel, Jan Struyf; 3(Dec):621--650, 2002.
[abs][pdf] [ps.gz] [ps]

Multiple Instance Learning of Real Valued Data: Daniel R. Dooly, Qi Zhang, Sally A. Goldman, Robert A. Amar; 3(Dec):651--678, 2002.
[abs][pdf] [ps.gz] [ps]

Learning Probabilistic Models of Link Structure: Lisa Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar; 3(Dec):679--707, 2002.
[abs][pdf] [ps.gz] [ps]

The Representational Power of Discrete Bayesian Networks: Charles X. Ling, Huajie Zhang; 3(Dec):709--721, 2002.
[abs][pdf] [ps.gz] [ps]

The Set Covering Machine: Mario Marchand, John Shawe Taylor; 3(Dec):723--746, 2002.
[abs][pdf] [ps.gz] [ps]

Coupled Clustering: A Method for Detecting Structural Correspondence: Zvika Marx, Ido Dagan, Joachim M. Buhmann, Eli Shamir; 3(Dec):747--780, 2002.
[abs][pdf] [ps.gz] [ps]

Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels: Prasanth B. Nair, Arindam Choudhury, Andy J. Keane; 3(Dec):781--801, 2002.
[abs][pdf] [ps.gz] [ps]

Lyapunov Design for Safe Reinforcement Learning: Theodore J. Perkins, Andrew G. Barto; 3(Dec):803--832, 2002.
[abs][pdf] [ps.gz] [ps]

Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling: Tobias Scheffer, Stefan Wrobel; 3(Dec):833--862, 2002.
[abs][pdf] [ps.gz] [ps]

Stopping Criterion for Boosting Based Data Reduction Techniques: from Binary to Multiclass Problem: Marc Sebban, Richard Nock, Stéphane Lallich; 3(Dec):863--885, 2002.
[abs][pdf] [ps.gz] [ps]

Learning to Construct Fast Signal Processing Implementations: Bryan Singer, Manuela Veloso; 3(Dec):887--919, 2002.
[abs][pdf] [ps.gz] [ps]

Policy Search using Paired Comparisons: Malcolm J. A. Strens, Andrew W. Moore; 3(Dec):921--950, 2002.
[abs][pdf] [ps.gz] [ps]

JMLR Special Issue on Computational Learning Theory

Introduction to the Special Issue on Computational Learning Theory: Philip M. Long; 3(Nov):361--362, 2002.
[abs][pdf] [ps.gz] [ps]

Tracking a Small Set of Experts by Mixing Past Posteriors: Olivier Bousquet, Manfred K. Warmuth; 3(Nov):363--396, 2002.
[abs][pdf] [ps.gz] [ps]

Using Confidence Bounds for Exploitation-Exploration Trade-offs: Peter Auer; 3(Nov):397--422, 2002.
[abs][pdf] [ps.gz] [ps]

Efficient Algorithms for Universal Portfolios: Adam Kalai, Santosh Vempala; 3(Nov):423--440, 2002.
[abs][pdf] [ps.gz] [ps]

Limitations of Learning Via Embeddings in Euclidean Half Spaces: Shai Ben-David, Nadav Eiron, Hans Ulrich Simon; 3(Nov):441--461, 2002.
[abs][pdf] [ps.gz] [ps]

Rademacher and Gaussian Complexities: Risk Bounds and Structural Results: Peter L. Bartlett, Shahar Mendelson; 3(Nov):463--482, 2002.
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On Boosting with Polynomially Bounded Distributions: Nader H. Bshouty, Dmitry Gavinsky; 3(Nov):483--506, 2002.
[abs][pdf] [ps.gz] [ps]

JMLR Special Issue on Shallow Parsing

Introduction to Special Issue on Machine Learning Approaches to Shallow Parsing: James Hammerton, Miles Osborne, Susan Armstrong, Walter Daelemans; 2(3):551--558, 2002.
[abs][pdf] [ps.gz] [ps]

Memory-Based Shallow Parsing: Erik F. Tjong Kim Sang; 2(3):559--594, 2002.
[abs][pdf] [ps.gz] [ps]

Shallow Parsing using Specialized HMMs: Antonio Molina, Ferran Pla; 2(3):595--613, 2002.
[abs][pdf] [ps.gz] [ps]

Text Chunking based on a Generalization of Winnow: Tong Zhang, Fred Damerau, David Johnson; 2(3):615--637, 2002.
[abs][pdf] [ps.gz] [ps]

Shallow Parsing with PoS Taggers and Linguistic Features: Beáta Megyesi; 2(3):639--668, 2002.
[abs][pdf] [ps.gz] [ps]

Learning Rules and Their Exceptions: Hervé Déjean; 2(3):669--693, 2002.
[abs][pdf] [ps.gz] [ps]

Shallow Parsing using Noisy and Non-Stationary Training Material: Miles Osborne; 2(3):695--719, 2002.
[abs][pdf] [ps.gz] [ps]

JMLR Special Issue on Kernel Methods

Introduction to the Special Issue on Kernel Methods (Kernel Machines Section): Nello Cristianini, John Shawe-Taylor, Robert C. Williamson; 2(12):95--96, 2001.
[abs][pdf] [ps.gz] [ps]

Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space (Kernel Machines Section): Roman Rosipal, Leonard J. Trejo; 2(12):97--123, 2001.
[abs][pdf] [ps.gz] [ps]

Support Vector Clustering (Kernel Machines Section): Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik; 2(12):125--137, 2001.
[abs][pdf] [ps.gz] [ps]
revision: Jan 2002
[abs] [pdf] [ps.gz] [ps]

One-Class SVMs for Document Classification (Kernel Machines Section): Larry M. Manevitz, Malik Yousef; 2(12):139--154, 2001.
[abs][pdf] [ps.gz] [ps]

Uniform Object Generation for Optimizing One-class Classifiers (Kernel Machines Section): David M.J. Tax, Robert P.W. Duin; 2(12):155--173, 2001.
[abs][pdf] [ps.gz] [ps]
errata: [pdf] [ps.gz] [ps]

A Generalized Kernel Approach to Dissimilarity-based Classification (Kernel Machines Section): Elzbieta Pekalska, Pavel Paclík, Robert P.W. Duin; 2(12):175--211, 2001.
[abs][pdf] [ps.gz] [ps]
revision: Nov 2002
[abs] [pdf] [ps.gz] [ps]

A New Approximate Maximal Margin Classification Algorithm (Kernel Machines Section): Claudio Gentile; 2(12):213--242, 2001.
[abs][pdf] [ps.gz] [ps]

Efficient SVM Training Using Low-Rank Kernel Representations (Kernel Machines Section): Shai Fine, Katya Scheinberg; 2(12):243--264, 2001.
[abs][pdf] [ps.gz] [ps]

On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines (Kernel Machines Section): Koby Crammer, Yoram Singer; 2(12):265--292, 2001.
[abs][pdf] [ps.gz] [ps]

Exact Simplification of Support Vector Solutions (Kernel Machines Section): Tom Downs, Kevin E. Gates, Annette Masters; 2(12):293--297, 2001.
[abs][pdf] [ps.gz] [ps]

Classes of Kernels for Machine Learning: A Statistics Perspective (Kernel Machines Section): Marc G. Genton; 2(12):299--312, 2001.
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