Learning SVM kernel with semi-definite programming

Abstract

It is well-known that the major task of the SVM approach lies in the selection of its kernel. The quality of kernel will determine the quality of SVM classifier directly. However, the best choice of a kernel for a given problem is still an open research issue. This paper presents a novel method which learns SVM kernel by transforming it into a standard semi-definite programming (SDP) problem and then solves this SDP problem using various existing methods. Experimental results are presented to prove that SVM with the kernel learned by our proposed method outperforms that with a single common kernel in terms of generalization power. © Springer-Verlag Berlin Heidelberg 2005.