On the fusion of polynomial kernels for support vector classifiers

Abstract

In this paper we propose some methods to build a kernel matrix for classification purposes using Support Vector Machines (SVMs) by fusing polynomial kernels. The proposed techniques have been successfully evaluated on artificial and real data sets. The new methods outperform the best individual kernel under consideration and they can be used as an alternative to the parameter selection problem in polynomial kernel methods. © Springer-Verlag Berlin Heidelberg 2006.