Atribución-NoComercial 4.0 InternacionalÁlvarez-Meza, Andrés MarinoDaza Santacoloma, GenaroAcosta Mejia, CarlosCastallanos Dominguez, German2019-06-262019-06-262012https://repositorio.unal.edu.co/handle/unal/31045In 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.application/pdfspaDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/Artículo de revistahttp://bdigital.unal.edu.co/21121/info:eu-repo/semantics/openAccessInformaticsElectrical and Electronic EngineeringParameter selectionLeast Squares-Support Vector MachinesMultidimensional Generalized Cross ValidationRegression.