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Multi-parametric Gaussian Kernel Function Optimization for ε-SVMr Using a Genetic Algorithm

  • J. Gascón-Moreno
  • E. G. Ortiz-García
  • S. Salcedo-Sanz
  • A. Paniagua-Tineo
  • B. Saavedra-Moreno
  • J. A. Portilla-Figueras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6692)

Abstract

In this paper we propose a novel multi-parametric kernel Support Vector Regression algorithm optimized with a genetic algorithm. The multi-parametric model and the genetic algorithm proposed are both described with detail in the paper. We also present experimental evidences of the good performance of the genetic algorithm, when compared to a standard Grid Search approach. Specifically, results in different real regression problems from public repositories have shown the good performance of the multi-parametric kernel approach both in accuracy and computation time.

Keywords

Genetic Algorithm Support Vector Machine Support Vector Regression Grid Search Good Individual 
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 2011

Authors and Affiliations

  • J. Gascón-Moreno
    • 1
  • E. G. Ortiz-García
    • 1
  • S. Salcedo-Sanz
    • 1
  • A. Paniagua-Tineo
    • 1
  • B. Saavedra-Moreno
    • 1
  • J. A. Portilla-Figueras
    • 1
  1. 1.Department of Signal Theory and CommunicationsUniversidad de AlcaláMadridSpain

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