Fusion of Gaussian Kernels Within Support Vector Classification

  • Javier M. Moguerza
  • Alberto Muñoz
  • Isaac Martín de Diego
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Javier M. Moguerza
    • 1
  • Alberto Muñoz
    • 2
  • Isaac Martín de Diego
    • 1
  1. 1.University Rey Juan CarlosMóstolesSpain
  2. 2.University Carlos III de MadridGetafeSpain

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