回归支持向量机的改进序列最小优化学习算法  

An Improved Sequential Minimal Optimization Learning Algorithm for Regression Support Vector Machine

作　　者：张浩然[1] 韩正之[1] 

机构地区：[1]上海交通大学自动化系,上海200030

出　　处：《软件学报》2003年第12期2006-2013,共8页Journal of Software

摘　　要：支持向量机(support vector machine,简称SVM)是一种基于结构风险最小化原理的学习技术,也是一种新的具有很好泛化性能的回归方法,提出了实现回归支持向量机的一种改进的SMO(sequential minimal optimization)算法,给出了两变量子优化问题的解析解,设计了新的工作集选择方法和停止条件,仿真实例说明,所提出的SMO算法比原始SMO算法具有更快的运算速度.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.

关 键 词：支持向量机 核方法 回归 序列最小优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]