Comparison of SVM and LS-SVM for regression

Support Vector Machines (SVM) has been widely used in classification and nonlinear function estimation. However, the major drawback of SVM is its higher computational burden for the constrained optimization programming. This disadvantage has been overcome by Least Squares Support Vector Machines (LS-SVM), which solves linear equations instead of a quadratic programming problem. This paper compares LS-SVM with SVM for regression. According to the parallel test results, conclusions can be made that LS-SVM is preferred especially for large scale problem, because its solution procedure is high efficiency and after pruning both sparseness and performance of LS-SVM are comparable with those of SVM.