An Improved Sequential Minimal Optimization Learning Algorithm for Regression Support Vector Machine
回归支持向量机的改进序列最小优化学习算法

Support vector machine (SVM) is a learning technique based on the structural risk minimization principle, and it is also a class of regression method with a good generalization ability. This paper presents an improved SMO (sequential minimal optimization) algorithm to train the regression SVM, which gives an the analytical solution to the QP problem of size two. A new working set selection method and a stopping condition are developed. The simulation results show that the improved SMO algorithm is significantly faster and more precise than the original SMO one.