Advances of Support Vector Machines(SVM)
支持向量机研究进展

Support vector machines(SVM) arc widespread attended for its excellent ability to learn, that arc based on statistical learning theory. But in dealing with large-scale quadratic programming ( QP) problem, traditional SVM will take too long time of training time, and has low efficiency and so on. This paper made a summarize of the new progress in the SVM training of algorithm, and made analysis and comparison on main algorithm, pointed out the advantages and disadvantages of them,focused on new progress in the current study-Fuzzy Support Vector Machine and Granular Support Vector Machine. Then the two mainly applications-classification and regression of SVM were discussed. Finally, the article gave the future research directions on SVM prediction.