JMLR 2001

21 papers

A Generalized Kernel Approach to Dissimilarity-Based Classification (Kernel Machines Section) Elzbieta Pekalska, Pavel Paclik, Robert P.W. Duin

A New Approximate Maximal Margin Classification Algorithm (Kernel Machines Section) Claudio Gentile

Bayes Point Machines (Kernel Machines Section) Ralf Herbrich, Thore Graepel, Colin Campbell

Classes of Kernels for Machine Learning: A Statistics Perspective (Kernel Machines Section) Marc G. Genton

Efficient SVM Training Using Low-Rank Kernel Representations (Kernel Machines Section) Shai Fine, Katya Scheinberg

Exact Simplification of Support Vector Solutions (Kernel Machines Section) Tom Downs, Kevin E. Gates, Annette Masters

Graph-Based Hierarchical Conceptual Clustering Istvan Jonyer, Diane J. Cook, Lawrence B. Holder

Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space (Kernel Machines Section) Roman Rosipal, Leonard J. Trejo

Lagrangian Support Vector Machines (Kernel Machines Section) O. L. Mangasarian, David R. Musicant

On the Algorithmic Implementation of Multiclass Kernel-Based Vector Machines (Kernel Machines Section) Koby Crammer, Yoram Singer

On the Influence of the Kernel on the Consistency of Support Vector Machines (Kernel Machines Section) Ingo Steinwart

On the Size of Convex Hulls of Small Sets Shahar Mendelson

One-Class SVMs for Document Classification (Kernel Machines Section) Larry M. Manevitz, Malik Yousef

Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms Robert E. Mahony, Robert C. Williamson

Regularized Principal Manifolds (Kernel Machines Section) Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson

Sparse Bayesian Learning and the Relevance Vector Machine Michael E. Tipping

Support Vector Clustering (Kernel Machines Section) Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik

Support Vector Machine Active Learning with Applications to Text Classification Simon Tong, Daphne Koller

SVMTorch: Support Vector Machines for Large-Scale Regression Problems (Kernel Machines Section) Ronan Collobert, Samy Bengio

Tracking the Best Linear Predictor Mark Herbster, Manfred K. Warmuth

Uniform Object Generation for Optimizing One-Class Classifiers (Kernel Machines Section) David M.J. Tax, Robert P.W. Duin