PARAMETER SELECTION IN LEAST SQUARES-SUPPORT VECTOR MACHINES REGRESSION ORIENTED, USING GENERALIZED CROSS-VALIDATION

摘要

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.