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 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.