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最小方差支撑向量回归 被引量：1

Minimum variance support vector regression
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摘要 基于支撑向量回归(SVR)可以通过构建支撑向量机分类问题实现的基本思想,推广最小类方差支撑向量机(MCVSVMs)于回归估计,提出了最小方差支撑向量回归(MVSVR)算法.该方法继承了MCVSVMs鲁棒性和泛化能力强的优点,分析了MVSVR和标准SVR之间的关系,讨论了在散度矩阵奇异情况下该方法的求解问题,同时也讨论了MVSVR的非线性情况.实验表明,该方法是可行的,且表现出了更强的泛化能力. Based on the basic idea that the support vector regression（SVR） can be regarded as a classification problem in the dual space,a regression algorithm,minimum variance support vector regression（MVSVR）,is proposed through constructing a classification problem by using the minimum class variance support vector machines（MCVSVMs）.This method inherits the characteristics of the MCVSVMs,and can be transformed into the traditional SVR.The linear and nonlinear cases of the MVSVR are discussed.Experimental results on the artificial and real datasets show the effectiveness of the MVSVR.
作者 王晓明 王士同
机构地区 江南大学信息工程学院
出处 《控制与决策》 EI CSCD 北大核心 2010年第4期556-561,共6页 Control and Decision
基金 国家863计划项目(2007AA1Z158 2006AA10Z313) 国家自然科学基金项目(60704047) 国家自然科学基金重大研宄计划项目(9082002)
关键词 关支撑向量回归 支撑向量机 最小类方差支撑向量机 Support vector regression Support vector machines Minimum class variance support vector machines
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献14

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同被引文献14

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控制与决策
2010年 第4期

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