Multi-parametric gaussian kernel function optimization for ε-SVMr using a genetic algorithm

Authors:
J. Gascón-Moreno;E. G. Ortiz-García;S. Salcedo-Sanz;A. Paniagua-Tineo;B. Saavedra-Moreno;J. A. Portilla-Figueras
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:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Year:
2011

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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.