An overview on twin support vector machines

被引：0
作者：
Shifei Ding
Junzhao Yu
Bingjuan Qi
Huajuan Huang
机构：
[1] China University of Mining and Technology,School of Computer Science and Technolog
[2] Chinese Academy of Science,Key Laboratory of Intelligent Information Processing, Institute of Computing Technology
[3] Beijing University of Posts and Telecommunications,Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia
来源：
Artificial Intelligence Review | 2014年 / 42卷
关键词：
Support vector machines; Twin support vector machines; Least squares twin support vector machines; Fuzzy twin support vector machines;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Twin support vector machines (TWSVM) is based on the idea of proximal SVM based on generalized eigenvalues (GEPSVM), which determines two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is 1/4 of standard SVM. In addition to keeping the superior characteristics of GEPSVM, the classification performance of TWSVM significantly outperforms that of GEPSVM. However, the stand-alone method requires the solution of two smaller quadratic programming problems. This paper mainly reviews the research progress of TWSVM. Firstly, it analyzes the basic theory and the algorithm thought of TWSVM, then tracking describes the research progress of TWSVM including the learning model and specific applications in recent years, finally points out the research and development prospects.
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页码：245 / 252
页数：7
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