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A Novel Estimation of the Regularization Parameter for ε-SVM

  • E. G. Ortiz-García
  • J. Gascón-Moreno
  • S. Salcedo-Sanz
  • A. M. Pérez-Bellido
  • J. A. Portilla-Figueras
  • L. Carro-Calvo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)

Abstract

This paper presents a novel way of estimating the regularization parameter C in regression ε-SVM. The proposed estimation method is based on the calculation of maximum values of the generalization and error loss function terms, present in the objective function of the SVM definition. Assuming that both terms must be optimized in approximately equal conditions in the objective function, we propose to estimate C as a comparison of the new model based on maximums and the standard SVM model. The performance of our approach is shown in terms of SVM training time and test error in several regression problems from well known standard repositories.

Keywords

Support Vector Machine Loss Function Regularization Parameter Support Vector Regression Support Vector Machine Model 
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 2009

Authors and Affiliations

  • E. G. Ortiz-García
    • 1
  • J. Gascón-Moreno
    • 1
  • S. Salcedo-Sanz
    • 1
  • A. M. Pérez-Bellido
    • 1
  • J. A. Portilla-Figueras
    • 1
  • L. Carro-Calvo
    • 1
  1. 1.Department of Signal Theory and CommunicationsUniversidad de AlcaláMadridSpain

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