Research on Algorithms of Constructing SVR Based on its Approximate Hyperplane
从近似超平面到SVR的算法研究

This paper contributes a class of algorithms of constructing SVR based on its different approximate hyperplanes. It proves that SVM has its approximate hyperplanes. According as there are Support Vectors near the SVM and the Support Vectors are consequentially near the approximate hyperplane of the SVM too, to bring forward the idea of constructing SVR starting from its approximate hyperplane to search the Support Vectors step by step. It represents an algorithm instance starting from the Multiple Linear Regression Model to constructing LS-SVM, based on the idea, and analyzes its complexity. Comparing between it, the method of solving LS-SVM with system of linear equations and the decomposition algorithm, the result is the algorithm can converge to LS-SVM and decrease the time complexity and reduce evidently the space complexity.