JMLR 2002

50 papers

A Robust Minimax Approach to Classification Gert R.G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan

Algorithmic Luckiness Ralf Herbrich, Robert C. Williamson

Cluster Ensembles --- a Knowledge Reuse Framework for Combining Multiple Partitions Alexander Strehl, Joydeep Ghosh

Coupled Clustering: A Method for Detecting Structural Correspondence Zvika Marx, Ido Dagan, Joachim M. Buhmann, Eli Shamir

Covering Number Bounds of Certain Regularized Linear Function Classes Tong Zhang

Data-Dependent Margin-Based Generalization Bounds for Classification András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi

Efficient Algorithms for Decision Tree Cross-Validation Hendrik Blockeel, Jan Struyf

Efficient Algorithms for Universal Portfolios Adam Kalai, Santosh Vempala

Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling Tobias Scheffer, Stefan Wrobel

Introduction to Special Issue on Machine Learning Approaches to Shallow Parsing James Hammerton, Miles Osborne, Susan Armstrong, Walter Daelemans

Kernel Independent Component Analysis (Kernel Machines Section) Francis R. Bach, Michael I. Jordan

Learning Equivalence Classes of Bayesian-Network Structures David Maxwell Chickering

Learning Monotone DNF from a Teacher That Almost Does Not Answer Membership Queries Nader H. Bshouty, Nadav Eiron

Learning Precise Timing with LSTM Recurrent Networks Felix A. Gers, Nicol N. Schraudolph, Jürgen Schmidhuber

Learning Probabilistic Models of Link Structure Lisa Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar

Learning Rules and Their Exceptions Hervé Déjean

Learning to Construct Fast Signal Processing Implementations Bryan Singer, Manuela Veloso

Limitations of Learning via Embeddings in Euclidean Half Spaces Shai Ben-David, Nadav Eiron, Hans Ulrich Simon

Lyapunov Design for Safe Reinforcement Learning Theodore J. Perkins, Andrew G. Barto

Machine Learning with Data Dependent Hypothesis Classes Adam Cannon, J. Mark Ettinger, Don Hush, Clint Scovel

Memory-Based Shallow Parsing Erik F. Tjong Kim Sang

Minimal Kernel Classifiers (Kernel Machines Section) Glenn M. Fung, Olvi L. Mangasarian, Alexander J. Smola

Multiple-Instance Learning of Real-Valued Data Daniel R. Dooly, Qi Zhang, Sally A. Goldman, Robert A. Amar

On Boosting with Polynomially Bounded Distributions Nader H. Bshouty, Dmitry Gavinsky

On Online Learning of Decision Lists Ziv Nevo, Ran El-Yaniv

On the Convergence of Optimistic Policy Iteration John N. Tsitsiklis

On Using Extended Statistical Queries to Avoid Membership Queries Nader H. Bshouty, Vitaly Feldman

Optimal Structure Identification with Greedy Search David Maxwell Chickering

PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification (Kernel Machines Section) Matthias Seeger

Policy Search Using Paired Comparisons Malcolm J. A. Strens, Andrew W. Moore

R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning Ronen I. Brafman, Moshe Tennenholtz

Rademacher and Gaussian Complexities: Risk Bounds and Structural Results Peter L. Bartlett, Shahar Mendelson

Recommender Systems Using Linear Classifiers Tong Zhang, Vijay S. Iyengar

Round Robin Classification Johannes Fürnkranz

Shallow Parsing Using Noisy and Non-Stationary Training Material Miles Osborne

Shallow Parsing Using Specialized HMMs Antonio Molina, Ferran Pla

Shallow Parsing with POS Taggers and Linguistic Features Beáta Megyesi

Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels Prasanth B. Nair, Arindam Choudhury, Andy J. Keane

Stability and Generalization Olivier Bousquet, André Elisseeff

Stopping Criterion for Boosting-Based Data Reduction Techniques: From Binary to Multiclass Problem Marc Sebban, Richard Nock, Stéphane Lallich

Text Chunking Based on a Generalization of Winnow Tong Zhang, Fred Damerau, David Johnson

Text Classification Using String Kernels Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Chris Watkins

The Learning-Curve Sampling Method Applied to Model-Based Clustering Christopher Meek, Bo Thiesson, David Heckerman

The Representational Power of Discrete Bayesian Networks Charles X. Ling, Huajie Zhang

The Set Covering Machine Mario Marchand, John Shawe-Taylor

The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces (Kernel Machines Section) Masashi Sugiyama, Klaus-Robert Müller

Tracking a Small Set of Experts by Mixing past Posteriors Olivier Bousquet, Manfred K. Warmuth

Using Confidence Bounds for Exploitation-Exploration Trade-Offs Peter Auer

Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski

Ε-MDPs: Learning in Varying Environments István Szita, Bálint Takács, András Lörincz