START JMLR Volume 1

JMLR Volume 1

Learning with Mixtures of Trees: Marina Meila, Michael I. Jordan; 1(Oct):1-48, 2000.
[abs] [pdf] [ps.gz] [ps] [html]

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.
[abs] [pdf] [ps.gz] [ps] [html]

Learning Evaluation Functions to Improve Optimization by Local Search: Justin Boyan, Andrew W. Moore; 1(Nov):77-112, 2000.
[abs] [pdf] [ps.gz] [ps]

Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers: Erin L. Allwein, Robert E. Schapire, Yoram Singer; 1(Dec):113-141, 2000.
[abs] [pdf] [ps.gz] [ps]

SVMTorch: Support Vector Machines for Large-Scale Regression Problems (Kernel Machines Section): Ronan Collobert, Samy Bengio; 1(Feb):143-160, 2001.
[abs] [pdf] [ps.gz] [ps] [html]

Lagrangian Support Vector Machines (Kernel Machines Section): O. L. Mangasarian, David R. Musicant; 1(Mar):161-177, 2001.
[abs] [pdf] [ps.gz] [ps] [html]

Regularized Principal Manifolds (Kernel Machines Section): Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson; 1(Jun):179-209, 2001.
[abs] [pdf] [ps.gz] [ps]

Sparse Bayesian Learning and the Relevance Vector Machine: Michael E. Tipping; 1(Jun):211-244, 2001.
[abs] [pdf] [ps.gz] [ps]

Bayes Point Machines (Kernel Machines Section): Ralf Herbrich, Thore Graepel, Colin Campbell; 1(Aug):245-279, 2001.
[abs] [pdf] [ps.gz] [ps]

Tracking the Best Linear Predictor: Mark Herbster, Manfred K. Warmuth; 1(Sep):281-309, 2001.
[abs] [pdf] [ps.gz] [ps]

Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms: Robert E. Mahony, Robert C. Williamson; 1(Sep):311-355, 2001.
[abs] [pdf] [ps.gz] [ps]

Editorial Board

JMLR Volume 2

JMLR Volume 2

On the Size of Convex Hulls of Small Sets: Shahar Mendelson; 2(Oct):1-18, 2001.
[abs] [pdf] [ps.gz] [ps]

Graph-Based Hierarchical Conceptual Clustering: Istvan Jonyer, Diane J. Cook, Lawrence B. Holder; 2(Oct):19-43, 2001.
[abs] [pdf] [ps.gz] [ps]

Support Vector Machine Active Learning with Applications to Text Classification: Simon Tong, Daphne Koller; 2(Nov):45-66, 2001.
[abs] [pdf] [ps.gz] [ps]

On the Influence of the Kernel on the Consistency of Support Vector Machines (Kernel Machines Section): Ingo Steinwart; 2(Nov):67-93, 2001.
[abs] [pdf] [ps.gz] [ps]

Introduction to the Special Issue on Kernel Methods (Kernel Machines Section): Nello Cristianini, John Shawe-Taylor, Robert C. Williamson; 2(Dec):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(Dec):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(Dec):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(Dec):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(Dec):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 Paclik, Robert P.W. Duin; 2(Dec):175-211, 2001.
[abs] [pdf] [ps.gz] [ps]
revision: Jan 2002
[abs] [pdf] [ps.gz] [ps]

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

Efficient SVM Training Using Low-Rank Kernel Representations (Kernel Machines Section): Shai Fine, Katya Scheinberg; 2(Dec):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(Dec):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(Dec):293-297, 2001.
[abs] [pdf] [ps.gz] [ps]

Classes of Kernels for Machine Learning: A Statistics Perspective (Kernel Machines Section): Marc G. Genton; 2(Dec):299-312, 2001.
[abs] [pdf] [ps.gz] [ps]

Recommender Systems Using Linear Classifiers: Tong Zhang, Vijay S. Iyengar; 2(Feb):313-334, 2002.
[abs] [pdf] [ps.gz] [ps]

Machine Learning with Data Dependent Hypothesis Classes: Adam Cannon, J. Mark Ettinger, Don Hush, Clint Scovel; 2(Feb):335-358, 2002.
[abs] [pdf] [ps.gz] [ps]

On Using Extended Statistical Queries to Avoid Membership Queries: Nader H. Bshouty, Vitaly Feldman; 2(Feb):359-395, 2002.
[abs] [pdf] [ps.gz] [ps]

The Learning-Curve Sampling Method Applied to Model-Based Clustering: Christopher Meek, Bo Thiesson, David Heckerman; 2(Feb):397-418, 2002.
[abs] [pdf] [ps.gz] [ps]

Text Classification using String Kernels: Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Chris Watkins; 2(Feb):419-444, 2002.
[abs] [pdf] [ps.gz] [ps]

Learning Equivalence Classes of Bayesian-Network Structures: David Maxwell Chickering; 2(Feb):445-498, 2002.
[abs] [pdf] [ps.gz] [ps]
errata: [pdf] [ps.gz] [ps]

Stability and Generalization: Olivier Bousquet, André Elisseeff; 2(Mar):499-526, 2002.
[abs] [pdf] [ps.gz] [ps]

Covering Number Bounds of Certain Regularized Linear Function Classes: Tong Zhang; 2(Mar):527-550, 2002.
[abs] [pdf] [ps.gz] [ps]

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

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

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

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

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

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

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

Round Robin Classification: Johannes Fürnkranz; 2(Mar):721-747, 2002.
[abs] [pdf] [ps.gz] [ps] [html]

Editorial Board

JMLR Volume 3

JMLR Volume 3

Kernel Independent Component Analysis (Kernel Machines Section): Francis R. Bach, Michael I. Jordan; 3(Jul):1-48, 2002.
[abs] [pdf] [ps.gz] [ps]

Learning Monotone DNF from a Teacher that Almost Does Not Answer Membership Queries: Nader H. Bshouty, Nadav Eiron; 3(Jul):49-57, 2002.
[abs] [pdf] [ps.gz] [ps]

On the Convergence of Optimistic Policy Iteration: John N. Tsitsiklis; 3(Jul):59-72, 2002.
[abs] [pdf] [ps.gz] [ps]

Data-dependent margin-based generalization bounds for classification: András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi; 3(Jul):73-98, 2002.
[abs] [pdf] [ps.gz] [ps]

Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components: Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski; 3(Aug):99-114, 2002.
[abs] [pdf] [ps.gz] [ps]

Learning Precise Timing with LSTM Recurrent Networks: Felix A. Gers, Nicol N. Schraudolph, Jürgen Schmidhuber; 3(Aug):115-143, 2002.
[abs] [pdf] [ps.gz] [ps]

ε-MDPs: Learning in Varying Environments: István Szita, Bálint Takács, András Lörincz; 3(Aug):145-174, 2002.
[abs] [pdf] [ps.gz] [ps] [html]

Algorithmic Luckiness: Ralf Herbrich, Robert C. Williamson; 3(Sep):175-212, 2002.
[abs] [pdf] [ps.gz] [ps]
errata: [pdf] [ps.gz] [ps]

R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning: Ronen I. Brafman, Moshe Tennenholtz; 3(Oct):213-231, 2002.
[abs] [pdf] [ps.gz] [ps]

PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification (Kernel Machines Section): Matthias Seeger; 3(Oct):233-269, 2002.
[abs] [pdf] [ps.gz] [ps]

On Online Learning of Decision Lists: Ziv Nevo, Ran El-Yaniv; 3(Oct):271-301, 2002.
[abs] [pdf] [ps.gz] [ps]

Minimal Kernel Classifiers (Kernel Machines Section): Glenn M. Fung, Olvi L. Mangasarian, Alexander J. Smola; 3(Nov):303-321, 2002.
[abs] [pdf] [ps.gz] [ps]

The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces (Kernel Machines Section): Masashi Sugiyama, Klaus-Robert Müller; 3(Nov):323-359, 2002.
[abs] [pdf] [ps.gz] [ps]

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.
[abs] [pdf] [ps.gz] [ps]

On Boosting with Polynomially Bounded Distributions: Nader H. Bshouty, Dmitry Gavinsky; 3(Nov):483-506, 2002.
[abs] [pdf] [ps.gz] [ps]

Optimal Structure Identification With Greedy Search: David Maxwell Chickering; 3(Nov):507-554, 2002.
[abs] [pdf] [ps.gz] [ps]
erratum: [pdf] [ps.gz] [ps]

A Robust Minimax Approach to Classification: Gert R.G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan; 3(Dec):555-582, 2002.
[abs] [pdf] [ps.gz] [ps]

Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions: Alexander Strehl, Joydeep Ghosh; 3(Dec):583-617, 2002.
[abs] [pdf] [ps.gz] [ps]

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]

Ultraconservative Online Algorithms for Multiclass Problems: Koby Crammer, Yoram Singer; 3(Jan):951-991, 2003.
[abs] [pdf] [ps.gz] [ps]

Latent Dirichlet Allocation: David M. Blei, Andrew Y. Ng, Michael I. Jordan; 3(Jan):993-1022, 2003.
[abs] [pdf] [ps.gz] [ps]

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]

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 (Kernel Machines Section): George Forman; 3(Mar):1289-1305, 2003.
[abs] [pdf] [ps.gz] [ps] full paper with appendices: [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] [demos]

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]

Editorial Board

JMLR Volume 4

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.
[abs][pdf][ps.gz][ps]

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.
[abs][pdf][ps.gz][ps]

Designing Committees of Models through Deliberate Weighting of Data Points: Stefan W. Christensen, Ian Sinclair, Philippa A. S. Reed; 4(Apr):39-66, 2003.
[abs][pdf][ps.gz][ps]

The em Algorithm for Kernel Matrix Completion with Auxiliary Data: Koji Tsuda, Shotaro Akaho, Kiyoshi Asai; 4(May):67-81, 2003.
[abs][pdf][ps.gz][ps]

Task Clustering and Gating for Bayesian Multitask Learning: Bart Bakker, Tom Heskes; 4(May):83-99, 2003.
[abs][pdf][ps.gz][ps] [code]

Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning: Dmitry Gavinsky; 4(May):101-117, 2003.
[abs][pdf][ps.gz][ps]

Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds: Lawrence K. Saul, Sam T. Roweis; 4(Jun):119-155, 2003.
[abs][pdf][ps.gz][ps]

On the Proper Learning of Axis-Parallel Concepts: Nader H. Bshouty, Lynn Burroughs; 4(Jun):157-176, 2003.
[abs][pdf][ps.gz][ps]

Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction: Mary Elaine Califf, Raymond J. Mooney; 4(Jun):177-210, 2003.
[abs][pdf][ps.gz][ps]

Tree Induction vs. Logistic Regression: A Learning-Curve Analysis: Claudia Perlich, Foster Provost, Jeffrey S. Simonoff; 4(Jun):211-255, 2003.
[abs][pdf][ps.gz][ps]

Learning Probabilistic Models: An Expected Utility Maximization Approach: Craig Friedman, Sven Sandow; 4(Jul):257-291, 2003.
[abs][pdf][ps.gz][ps]

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.
[abs][pdf][ps.gz][ps]

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]

Learning Behavior-Selection by Emotions and Cognition in a Multi-Goal Robot Task: Sandra Clara Gadanho; 4(Jul):385-412, 2003.
[abs][pdf][ps.gz][ps]

Introduction to the Special Issue on Inductive Logic Programming: James Cussens, Alan M. Frisch; 4(Aug):413-414, 2003.
[abs][pdf][ps.gz][ps]

ILP: A Short Look Back and a Longer Look Forward: David Page, Ashwin Srinivasan; 4(Aug):415-430, 2003.
[abs][pdf][ps.gz][ps]

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]

On Inclusion-Driven Learning of Bayesian Networks: Robert Castelo, Tomás Kocka; 4(Sep):527-574, 2003.
[abs][pdf][ps.gz][ps]

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.
[abs][pdf][ps.gz][ps]

Inducing Grammars from Sparse Data Sets: A Survey of Algorithms and Results: Orlando Cicchello, Stefan C. Kremer; 4(Oct):603-632, 2003.
[abs][pdf][ps.gz][ps]

Smooth Boosting and Learning with Malicious Noise: Rocco A. Servedio; 4(Sep):633-648, 2003.
[abs][pdf][ps.gz][ps]

Speedup Learning for Repair-based Search by Identifying Redundant Steps: Shaul Markovitch, Asaf Shatil; 4(Sep):649-682, 2003.
[abs][pdf][ps.gz][ps]

Comparing Bayes Model Averaging and Stacking When Model Approximation Error Cannot be Ignored: Bertrand Clarke; 4(Oct):683-712, 2003.
[abs][pdf][ps.gz][ps]

Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity: Shie Mannor, Ron Meir, Tong Zhang; 4(Oct):713-742, 2003.
[abs][pdf][ps.gz][ps]

Tree-Structured Neural Decoding: Christian d'Avignon, Donald Geman; 4(Oct):743-754, 2003.
[abs][pdf][ps.gz][ps]

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.
[abs][pdf][ps.gz][ps]

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.
[abs][pdf][ps.gz][ps]

On the Rate of Convergence of Regularized Boosting Classifiers: Gilles Blanchard, Gábor Lugosi, Nicolas Vayatis; 4(Oct):861-894, 2003.
[abs][pdf][ps.gz][ps]

Concentration Inequalities for the Missing Mass and for Histogram Rule Error: David McAllester, Luis Ortiz; 4(Oct):895-911, 2003.
[abs][pdf][ps.gz][ps]

Learning over Sets using Kernel Principal Angles (Kernel Machines Section): Lior Wolf, Amnon Shashua; 4(Oct):913-931, 2003.
[abs][pdf][ps.gz][ps]

An Efficient Boosting Algorithm for Combining Preferences: Yoav Freund, Raj Iyer, Robert E. Schapire, Yoram Singer; 4(Nov):933-969, 2003.
[abs][pdf][ps.gz][ps]

Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet: Marcus Hutter; 4(Nov):971-1000, 2003.
[abs][pdf][ps.gz][ps]

A Unified Framework for Model-based Clustering: Shi Zhong, Joydeep Ghosh; 4(Nov):1001-1037, 2003.
[abs][pdf][ps.gz][ps]

Nash Q-Learning for General-Sum Stochastic Games: Junling Hu, Michael P. Wellman; 4(Nov):1039-1069, 2003.
[abs][pdf][ps.gz][ps]

Sparseness of Support Vector Machines: Ingo Steinwart; 4(Nov):1071-1105, 2003.
[abs][pdf][ps.gz][ps]

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]

Editorial Board

JMLR Volume 5

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]

Editorial Board

JMLR Volume 6

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]

</