A New Support Vector Regression Model

Support vector machines (SVM) is a new kind of intelligent machine presented by Vapnik and his study group in the middle of the 1990Th. SVM is based on statistical learning theory developed in the 1970Th. It embodies the theory of structure risk minimization (SRM). Because it has quite perfect theoretical properties and good learning performance, and can solve some practical problems with low sample size, non-linearity, high-dimension of feature space and local minimization, SVM becomes a hot spot of machine learning theory.SVM has successful applications in many fields, such as pattern recognition, regression estimation, function approaching and so on. However, as a new technique, SVM still has many problems that need to be studied and improved, and researches in regression estimation based on SVM need to be enhanced. How to design fast and efficient SVM algorithms applied to regression estimation becomes a great challenge in practical applications of support vector machines.In this paper, we have learned some regress algorithms and proposed several developed Support Vector Regression (SVR). The main works are as follows:First of all, the principles of SVM are reviewed and the relationship between Least Square Estimation (LSE) and regression estimation algorithms of SVM (SVR) are compared and analyzed. Secondly, an improved regression estimation algorithm named SVR-LS is presented by theoretical deduction. And then, the proposed SVR-LS algorithm is applied to approaching the function of Sinc. Comparing to experimental results of the SVR and LES algorithm, we find that the new algorithm has good effect in function approaching. Finally, the proposed SVR-LS algorithm and normal regression estimation algorithm of SVM are applied to regression estimation of two different data sets, and learning speed and learning precision in regression are compared between the two algorithms. The experimental results show that the proposed SVR-LS algorithm is better than the normal regression estimation algorithm of SVM.