基于最小二乘的最小类方差支撑向量机  

Least-Square-based Minimum Class Variance Support Vector Machines

作　　者：王晓明[1] 王士同[1] 

机构地区：[1]江南大学信息工程学院,无锡214122

出　　处：《计算机工程》2010年第12期19-21,共3页Computer Engineering

基　　金：国家自然科学基金资助重大项目(9082002);国家自然科学基金资助项目(60704047);国家"863"计划基金资助项目(2007AA1Z158)

摘　　要：针对最小类方差支撑向量机(MCVSVM)在小样本情况下仅利用类内散度矩阵非零空间中信息的问题,提出基于最小二乘的最小类方差支撑向量机(LS-MCVSVM)算法,通过牛顿优化法迭代求解LS-MCVSVM的优化问题,从而有效解决了小样本问题。实验结果表明,相对于MCVSVM,LS-MCVSVM算法可进一步提高泛化能力,减少训练时间开销。Aiming at the problem that Minimum Class Variance Support Vector Machines（MCVSVM） which utilize only information in the non-null space of the within-class scatter matrix in small sample size case, this paper presents a novel algorithm called Least-Square-based Minimum Class Variance Support Vector Machines（LS-MCVSVM）. The optimization problem of LS-MCVSVM can be solved by using Newton optimization, and the small sample problem can be avoided efficiently. Experimental results on several real datasets show that LS-MCVSVM can improve the generating ability and reduce the training time greatly.

关 键 词：监督学习 最小类方差支撑向量机 优化算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]