Optimal double-kernel combination for classification

Abstract

Traditional kernelised classification methods could not perform well sometimes because of the using of a single and fixed kernel, especially on some complicated data sets. In this paper, a novel optimal double-kernel combination (ODKC) method is proposed for complicated classification tasks. Firstly, data sets are mapped by two basic kernels into different feature spaces respectively, and then three kinds of optimal composite kernels are constructed by integrating information of the two feature spaces. Comparative experiments demonstrate the effectiveness of our methods. © 2009 Springer Berlin Heidelberg.