An Improved LSSVM Regression Algorithm

Support vector machine (SVM) is a new and valid machine-learning algorithm developed on statistical learning theory, and it has been used for classification, function regression, and time series prediction. Recently an extension of traditional SVM named LSSVM has been introduced. Compared with the support vector machine, the least squares support vector machine (LSSVM) lose the sparseness, which would influence the efficiency of relearning. To conclude a sparse solution, in this paper we present an improved algorithm for least squares support vector machine - XS-LSSVM, and prove its effect by an simulation experiment.