Fusion of gaussian kernels within support vector classification

Authors:
Javier M. Moguerza;Alberto Muñoz;Isaac Martín de Diego
Affiliations:
University Rey Juan Carlos, Móstoles, Spain;University Carlos III de Madrid, Getafe, Spain;University Rey Juan Carlos, Móstoles, Spain
Venue:
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2006

Citing 7
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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 Gaussian 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 Gaussian kernel methods.