一种改进的支持向量机BS-SVM  

An Improved SVM:BS-SVM

作　　者：郭亚琴[1] 王正群[2] 

机构地区：[1]紫琅职业技术学院,江苏南通226002 [2]扬州大学信息工程学院,江苏扬州225009

出　　处：《微电子学与计算机》2010年第6期54-56,共3页Microelectronics & Computer

基　　金：国家自然科学基金项目(60875004);江苏省高校自然科学基础研究项目(07KJB520133);紫琅职业技术学院科研项目(科研2008003)

摘　　要：提出了一种改进的SVM:BS-SVM,它先对训练样本进行分类,根据每个样本到模式类样本均值的距离,将训练样本分为三种:好样本、差样本、边界样本,然后用边界样本训练得到分类器.实验表明,BS-SVM相比SVM在分类正确率、分类速度以及使用的样本规模上都表现出了一定的优越性.A support vector machine constructs an optimal hyperplane from a small set of samples near the boundary.This makes it sensitive to these specific samples and tends to result in machines either too complex with poor generalization ability or too imprecise with high training error,depending on the kernel parameters.SVM focuses on the samples near the boundary in training time,and those samples intermixed in another class are usually no good to improve the classifier＇s performance,instead they may greatly increase the burden of computation.In order to improve the generalization ability we present an improved SVM：BS-SVM.It first classifies the training set.According to the distance between the sample and the mean sample,the training sample is classified three classes：good sample,poor sample and boundary sample,then trains the SVM with boundary sample.Experimental results show that BS-SVM is better than SVM in speed and accuracy of classification.

关 键 词：支持向量机 训练样本 样本分类 边界样本 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]