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.
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Ortiz-García, E.G., Gascón-Moreno, J., Salcedo-Sanz, S., Pérez-Bellido, A.M., Portilla-Figueras, J.A., Carro-Calvo, L. (2009). A Novel Estimation of the Regularization Parameter for ε-SVM. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_5
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DOI: https://doi.org/10.1007/978-3-642-04394-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04393-2
Online ISBN: 978-3-642-04394-9
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