Improved Regression Estimation Algorithm Of SVM And Its Applications

Support vector machines (SVM) is a new kind of intelligent machine presented by Vapnik and his study group in the middle of the 1990'th. SVM is based on statistical learning theory developed in the 1970'th. 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, first of all, the principles of SVM are reviewed and some regression estimation algorithms of SVM are compared. Secondly, an improved regression estimation algorithm of SVM - Successive Overrelaxiation for Support Vector Regression Machines (SORR) is presented by theoretical deduction, and then, the proposed SORR algorithm and normal regression estimation algorithm of SVM are applied to determine serum cholesterol levels from the measurements of spectral content of three blood samples in medical science. Finally, learning speed and learning precision in regression are compared between the two algorithms. The experimental results show that the proposed SORR algorithm is better than the normal regression estimation algorithm of SVM.