JMLR 2001
22 papers
Exact Simplification of Support Vector Solutions
Tom Downs, Kevin E. Gates, Annette Masters
On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines
Koby Crammer, Yoram Singer
Efficient SVM Training Using Low-Rank Kernel Representations
Shai Fine, Katya Scheinberg
A New Approximate Maximal Margin Classification Algorithm
Claudio Gentile
A Generalized Kernel Approach to Dissimilarity-based Classification
Elzbieta Pekalska, Pavel Paclik, Robert P.W. Duin
Uniform Object Generation for Optimizing One-class Classifiers
David M.J. Tax, Robert P.W. Duin
One-Class SVMs for Document Classification
Larry M. Manevitz, Malik Yousef
Support Vector Clustering
Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space
Roman Rosipal, Leonard J. Trejo
Introduction to the Special Issue on Kernel Methods
Nello Cristianini, John Shawe-Taylor, Robert C. Williamson
Support Vector Machine Active Learning with Applications to Text Classification
Simon Tong, Daphne Koller
Graph-Based Hierarchical Conceptual Clustering
Istvan Jonyer, Diane J. Cook, Lawrence B. Holder
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
Tracking the Best Linear Predictor
Mark Herbster, Manfred K. Warmuth
Bayes Point Machines
Ralf Herbrich, Thore Graepel, Colin Campbell
Sparse Bayesian Learning and the Relevance Vector Machine
Michael E. Tipping
Regularized Principal Manifolds
Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson
Lagrangian Support Vector Machines
O. L. Mangasarian, David R. Musicant
SVMTorch: Support Vector Machines for Large-Scale Regression Problems
Ronan Collobert, Samy Bengio