PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dual unification of bi-class support vector machine formulations
Luis Gonzalez, Cecilio Angulo, Francisco Velasco and Andreu Catala
Pattern Recognition Volume 39, Number 7, pp. 1325-1332, 2006. ISSN 0031-3203

Abstract

Support Vector Machine (SVM) theory was originally developed on the basis of a linearly separable binary classification problem, and other approaches have been later introduced for this problem. In this paper it is demonstrated that all these approaches admit the same dual problem formulation in the linearly separable case and that all the solutions are equivalent. For the non-linearly separable case, all the approaches can also be formulated as a unique dual optimization problem, however their solutions are not equivalent. Discussions and remarks in the article point to an in-depth comparison between SVM formulations and associated parameters.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2220
Deposited By:Cecilio Angulo
Deposited On:01 October 2006