Services on Demand
Journal
Article
Indicators
- Cited by SciELO
- Access statistics
Related links
- Cited by Google
- Similars in SciELO
- Similars in Google
Share
DYNA
Print version ISSN 0012-7353On-line version ISSN 2346-2183
Abstract
ALVAREZ MEZA, ANDRÉS M.; DAZA SANTACOLOMA, GENARO; ACOSTA MEDINA, CARLOS D. and 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.
Keywords : parameter selection; least squares-support vector machines; multidimensional generalized cross validation; regression.