Total margin algorithms in support vector machines

被引：0
|
作者：
Yoon, M [1 ]
Yun, YB
Nakayama, H
机构：
[1] Kagawa Univ, Fac Engn, Takamatsu, Kagawa 7610396, Japan
[2] Konan Univ, Kobe, Hyogo 6588501, Japan
来源：
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2004年 / E87D卷 / 05期
关键词：
support vector machine; generalization ability; soft margin; slack variable; total margin; surplus variable;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Support vector algorithms try to maximize the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithms which consider the distance between all data points and the separating hyperplane. The method extends and modifies the existing algorithms. Experimental studies show that the total margin algorithms provide good performance comparing with the existing support vector algorithms.
引用
收藏
页码：1223 / 1230
页数：8
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