Parameter Selection in Least Squares-Support Vector Machines Regression Oriented, Using Generalized Cross-Validation

• Published in: Dyna (Medellín, Colombia) Vol. 79; no. 171; pp. 23 - 30
• Main Authors: ÁLVAREZ MEZA, ANDRÉS M., DAZA SANTACOLOMA, GENARO, ACOSTA MEDINA, CARLOS D., CASTELLANOS DOMÍNGUEZ, GERMÁN
• Format: Journal Article
• Language: English
• Published: Bogota Universidad Nacional de Colombia 01-02-2012
• Subjects: ENGINEERING, MULTIDISCIPLINARY; least squares; multidimensional generalized cross validation; máquinas de vectores de soporte; mínimos cuadrados; parameter selection; regresión; regression; selección de parámetros; support vector machines; validación cruzada generalizada multidimensional; least squares-support vector machines; mínimos cuadrados-máquinas de vectores de soporte; multidimensional generalized cross validation; regression; regresión; validación cruzada generalizada multidimensional; parameter selection; selección de parámetros
• ISSN: 0012-7353; 2346-2183

In this work a new methodology for automatic selection of the free parameters in the Least Squares-Support Vector Machines (LS-SVM) regression oriented algorithm is proposed. We employ a multidimensional Generalized Cross-Validation analysis in the linear equation system of LS-SVM. Our approach does not require a prior knowledge about the influence of the LS-SVM free parameters in the results. The methodology is tested on two artificial and two real-world data sets. According to the results our methodology computes suitable regressions with competitive relative errors.