一种基于重复训练的支持向量机方法

计算机工程与应用 ›› 2007, Vol. 43 ›› Issue (31): 165-168.

一种基于重复训练的支持向量机方法

吴巧敏,林亚平

湖南大学计算机与通信学院,长沙 410082

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-11-01 发布日期:2007-11-01
通讯作者: 吴巧敏

Support vector machine based on training repeatedly

WU Qiao－min,LIN Ya－ping

The College of Computer and Communication,Hunan University,Changsha 410082,China

Received:1900-01-01 Revised:1900-01-01 Online:2007-11-01 Published:2007-11-01
Contact: WU Qiao－min

摘要/Abstract

摘要： 针对支持向量机中存在的对噪音和野值敏感的问题,提出了一种基于重复训练的支持向量机方法。该方法选取重复训练后会对分类面有影响的样本,根据其类别隶属度,重复训练相应的次数,以此来改变样本的权值,减小噪音和野值的影响。将该算法应用于文本分类中,实验结果表明,该方法在适度增加了训练时间的情况下,不仅比标准支持向量机方法具有更好的抗噪音和野值的能力,而且提高了分类性能。

Abstract: Since SVM is very sensitive to outliers and noises in the training set,a support vector machine algorithm based on training repeatedly is proposed in this paper.Samples having effects on decision surface after being trained repeatedly are chosen.And then they are trained repeatedly for some times according to their fuzzy membership.The weight of these samples is changed by this way and reduced in the influence of outliers and noises.The improved SVM algorithm is employed to text categorization,though the training time is increased,better effect is obtained than the traditional support vector machine,and this method effectively distinguishes between the valid samples and the outliers or noises．

吴巧敏,林亚平. 一种基于重复训练的支持向量机方法[J]. 计算机工程与应用, 2007, 43(31): 165-168.

WU Qiao－min,LIN Ya－ping. Support vector machine based on training repeatedly[J]. Computer Engineering and Applications, 2007, 43(31): 165-168.