Resumo

ALVAREZ MEZA, ANDRÉS M.; DAZA SANTACOLOMA, GENARO; ACOSTA MEDINA, CARLOS D.  e  CASTELLANOS DOMINGUEZ, GERMÁN. PARAMETER SELECTION IN LEAST SQUARES-SUPPORT VECTOR MACHINES REGRESSION ORIENTED, USING GENERALIZED CROSS-VALIDATION. Dyna rev.fac.nac.minas [online]. 2012, vol.79, n.171, pp.23-30. ISSN 0012-7353.

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 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.

Palavras-chave : parameter selection; least squares-support vector machines; multidimensional generalized cross validation; regression.

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