Improving Support Vector Classification via the Combination of Multiple Sources of Information

  • Javier M. Moguerza
  • Alberto Muñoz
  • Isaac Martín de Diego
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

In this paper we describe several new methods to build a kernel matrix from a collection of kernels. This kernel will be used for classification purposes using Support Vector Machines (SVMs). The key idea is to extend the concept of linear combination of kernels to the concept of functional (matrix) combination of kernels. The functions involved in the combination take advantage of class conditional probabilities and nearest neighbour techniques. The proposed methods have been successfully evaluated on a variety of real data sets against a battery of powerful classifiers and other kernel combination techniques.

Keywords

Support Vector Machine Kernel Matrix Multivariate Adaptative Regression Spline Machine Learn Research Handwritten Digit Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

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

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