On the fusion of polynomial kernels for support vector classifiers

Authors:
Isaac Martín de Diego;Javier M. Moguerza;Alberto Muñoz
Affiliations:
University Rey Juan Carlos, Móstoles, Spain;University Rey Juan Carlos, Móstoles, Spain;University Carlos III de Madrid, Getafe, Spain
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

Citing 6
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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.