Multiclass Lagrangian support vector machine

被引：2
|
作者：
Jae Pil Hwang
Baehoon Choi
In Wha Hong
Euntai Kim
机构：
[1] Yonsei University,School of Electrical and Electronic Engineering
[2] Korea Electronics Technology Institute,undefined
来源：
Neural Computing and Applications | 2013年 / 22卷
关键词：
SVM; Multiclass problem; Lagrangian SVM; Training method;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A support vector machine (SVM) has been developed for two-class problems, although its application to multiclass problems is not straightforward. This paper proposes a new Lagrangian SVM (LSVM) for application to multiclass problems. The multiclass Lagrangian SVM is formulated as a single optimization problem considering all the classes together, and a training method tailored to the multiclass problem is presented. A multiclass output representation matrix is defined to simplify the optimization formulation and associated training method. The proposed method is applied to some benchmark datasets in repository, and its effectiveness is demonstrated via simulation.
引用
收藏
页码：703 / 710
页数：7
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