Papers
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
Masashi Sugiyama, Klaus-Robert Müller
Tracking a Small Set of Experts by Mixing Past Posteriors
Olivier Bousquet, Manfred K. Warmuth
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
A Generalized Kernel Approach to Dissimilarity-based Classification
Elzbieta Pekalska, Pavel Paclik, Robert P.W. Duin
A New Approximate Maximal Margin Classification Algorithm
Claudio Gentile
Bayes Point Machines
Ralf Herbrich, Thore Graepel, Colin Campbell
Efficient SVM Training Using Low-Rank Kernel Representations
Shai Fine, Katya Scheinberg
Exact Simplification of Support Vector Solutions
Tom Downs, Kevin E. Gates, Annette Masters
Graph-Based Hierarchical Conceptual Clustering
Istvan Jonyer, Diane J. Cook, Lawrence B. Holder
Introduction to the Special Issue on Kernel Methods
Nello Cristianini, John Shawe-Taylor, Robert C. Williamson
Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space
Roman Rosipal, Leonard J. Trejo
Lagrangian Support Vector Machines
O. L. Mangasarian, David R. Musicant
One-Class SVMs for Document Classification
Larry M. Manevitz, Malik Yousef
On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines
Koby Crammer, Yoram Singer
On the Size of Convex Hulls of Small Sets
Shahar Mendelson
Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms
Robert E. Mahony, Robert C. Williamson
Regularized Principal Manifolds
Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf et al.
Sparse Bayesian Learning and the Relevance Vector Machine
Michael E. Tipping
Support Vector Clustering
Asa Ben-Hur, David Horn, Hava T. Siegelmann et al.