%0 Journal Article
%T PARAMETER SELECTION IN LEAST SQUARES-SUPPORT VECTOR MACHINES REGRESSION ORIENTED, USING GENERALIZED CROSS-VALIDATION
%A ¨¢LVAREZ MEZA
%A ANDR¨¦S M.
%A DAZA SANTACOLOMA
%A GENARO
%A ACOSTA MEDINA
%A CARLOS D.
%A CASTELLANOS DOM¨ªNGUEZ
%A GERM¨¢N
%J DYNA
%D 2012
%I Universidad Nacional de Colombia
%X 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.
%K parameter selection
%K least squares-support vector machines
%K multidimensional generalized cross validation
%K regression.
%U http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S0012-73532012000100003&lng=en&nrm=iso&tlng=en