An empirical assessment of kernel type performance for least squares support vector machine classifiers
Bart Baesens, Stijn Viaene, Tony Van Gestel, Johan A. K. Suykens, Guido Dedene, Bart De Moor, Jan Vanthienen · 2002
Recently, a modified version of support vector machines (SVMs), least-squares SVM (LS-SVM) classifiers, has been introduced, which is closely related to a form of ridge regression-type SVMs. In LS-SVMs, the classifier is obtained as the solution to a linear system instead of a quadratic programming problem. In this paper, UCI (University of California at Irvine) benchmark data sets are used to evaluate the performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. The hyperparameters of the LS-SVM problem formulation are tuned using a 10-fold cross-validation procedure and a grid search mechanism. When comparing the performance of a nonlinear (RBF or polynomial) LS-SVM classifier with that of a linear LS-SVM, additional insight can be gained into the degree of nonlinearity of the classification problem at hand. Using a statistical motivation, it is concluded that RBF LS-SVM classifiers consistently yield among the best results for each data set.