PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Multi-Class Support Vector Machine Based on Scatter Criteria
Robert Jenssen, Marius Kloft, Alexander Zien, Sören Sonnenburg and Klaus-Robert Müller
(2009) Technical University Berlin, Berlin, Germany.

Abstract

We re-visit Support Vector Machines (SVMs) and provide a novel interpretation thereof in terms of weighted class means and scatter theory. The gained theoretical insight can be translated into a highly efficient extension to multi-class SVMs: mScatter-SVMs. Numerical simulations reveal that more than an order of magnitude speed-up can be gained while the classification performance remains largely unchanged at the level of the classical one vs. rest and one vs. one implementation of multi-class SVMs.

EPrint Type:Other
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5476
Deposited By:Alexander Zien
Deposited On:10 October 2009