摘要： 为提高支持向量回归的运算速度，提出一种双支持向量回归的牛顿算法。求解2个只带一组约束的支持向量问题，以减少运算量，将2个约束优化问题转化为无约束最优化问题，并采用牛顿迭代算法求解。实验结果表明，在保证与支持向量回归和双支持向量回归拟合能力相当的同时，该算法能减少训练时间。

关键词: 机器学习, 模式识别, 支持向量回归, 双支持向量回归, 无约束优化, 牛顿算法

Abstract: For improving the learning speed of Support Vector Regression(SVR), this paper proposes a Newton algorithm for Twin Support Vector Regression(TSVR) that tries to find a pair of nonparallel planes by solving two related SVR-type problems and converts the classical Quadratic Programming Problem(QPP) to two small unconstrained optimization problems. Each of the unconstrained optimization problems is solved by Newton algorithm. Experimental results show that the proposed algorithm has good fitting ability as SVR and TSVR, and can reduce the training time.

Key words: machine learning, pattern recognition, Support Vector Regression(SVR), Twin Support Vector Regression(TSVR), unconstrained optimization, Newton algorithm

中图分类号: 

• TP301.6