[1]薛松,李雷. 基于类内离散度的最小二乘支持向量机[J].计算机技术与发展,2015,25(04):71-74.
 XUE Song,LI Lei. Least Squares Support Vector Machine Based on Within-class Scatter[J].,2015,25(04):71-74.
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 基于类内离散度的最小二乘支持向量机()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年04期
页码:
71-74
栏目:
智能、算法、系统工程
出版日期:
2015-04-10

文章信息/Info

Title:
 Least Squares Support Vector Machine Based on Within-class Scatter
文章编号:
1673-629X(2015)04-0071-04
作者:
 薛松李雷
 南京邮电大学 理学院
Author(s):
 XUE SongLI Lei
关键词:
 类内离散度分类最小二乘支持向量机核方法
Keywords:
 within-class scatterclassificationleast squares support vector machinekernel methods
分类号:
TP31
文献标志码:
A
摘要:
 支持向量机是一种很流行的机器学习方法,在许多领域都有了广泛的使用。传统的支持向量机模型是寻求类之间的间隔最大化,而忽视了一个重要的信息—样本的类内结构,类内离散度。文中将Fisher判别分析里面的类内离散度引入到最小二乘支持向量机中,提出了基于类内离散度的最小二乘支持向量机模型。并通过核函数将样本映射到高维特征空间,在特征空间中进行样本分类。基于UCI数据库的数据集实验测试表明,基于类内离散度的最小二乘支持向量机提高了分类的准确度。
Abstract:
 Support vector machine ( SVM) is a popular machine learning technique,and it has been widely used in many real-world ap-plications. Traditional SVMs aims at seeking the hyperplane that maximizes the margin and ignores an important prior knowledge, the within-class structure. It formulates a Least Square Support Vector Machine ( LSSVM) based on within-class scatter for binary classifi-cation,which incorporates minimum within-class scatter in Fisher Discriminant Analysis ( FDA) into LSSVM. The sample points are mapped to a high dimensional feature space where the samples are classified by using the kernel method. Experiments on four benchmark-ing datasets based on UCI show that the proposed WCS-LSSVM can improve the classification accuracy.

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更新日期/Last Update: 2015-06-04