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

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...

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Bibliographic Details
Published inDyna (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
LanguageEnglish
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
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Summary: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.
ISSN:0012-7353
2346-2183