A novel estimation of the regularization parameter for Ɛ-SVM

Authors:
E. G. Ortiz-García;J. Gascón-Moreno;S. Salcedo-Sanz;A. M. Pérez-Bellido;J. A. Portilla-Figueras;L. Carro-Calvo
Affiliations:
Department of Signal Theory and Communications, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

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Abstract

This paper presents a novel way of estimating the regularization parameter C in regression Ɛ-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.