HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Thies, T. Articles by Weber, F. Search for Related Content PubMed PubMed Citation Articles by Thies, T. Articles by Weber, F.

Optimal Reduced-Set Vectors for Support Vector Machines with a Quadratic Kernel

thorsten.thies{at}cognitec.com, Cognitec Systems GmbH, D–01139 Dresden, Germany

Frank Weber

frank.weber{at}cognitec.com, Cognitec Systems GmbH, D–01139 Dresden, Germany

To reduce computational cost, the discriminant function of a support vector machine (SVM) should be represented using as few vectors as possible. This problem has been tackled in different ways. In this article, we develop an explicit solution in the case of a general quadratic kernel k(x,x')=(C+D x x')². For a given number of vectors, this solution provides the best possible approximation and can even recover the discriminant function if the number of used vectors is large enough. The key idea is to express the inhomogeneous kernel as a homogeneous kernel on a space having one dimension more than the original one and to follow the approach of Burges (1996).