支持向量机分类算法研究  

Support vector machines based classification algorithm

作　　者：周宽久[1] 张世荣[1] 

机构地区：[1]大连理工大学软件学院,辽宁大连116620

出　　处：《计算机工程与应用》2009年第1期159-162,182,共5页Computer Engineering and Applications

基　　金：国家自然科学基金~~

摘　　要：支持向量机在处理两类分类问题时,当两类样本混杂严重时会降低分类精度。在NN-SVM分类算法的基础上,通过计算样本点与其最近邻点类别的异同以及该点与其k个同类近邻点在核空间的平均距离修剪混淆点,进而提出了一种改进的NN-SVM算法——KCNN-SVM。实验数据表明,KCNN-SVM算法与SVM以及NN-SVM相比,有着更高的分类精度和更快的训练、分类时间。The accuracy of classification of SVM in a two-class classification problem would be decreased because of those promiscuous samples.KCNN-SVM is proposed in this paper as an improved NN-SVM algorithm,which prunes a sample according to their nearest neighbor＇s class label as well as the average distance in kernel space between it and its k congener nearest neighbors.Experimental results show that KCNN-SVM algorithm is better than both SVM and NN-SVM in accuracy of classification and the total training and testing time is comparative to that of NN-SVM.

关 键 词：支持向量机 核空间 文本分类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]