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Annals of Data Science
Published in: Annals of Data Science 2/2014

01-06-2014

Review on: Twin Support Vector Machines

Authors: Yingjie Tian, Zhiquan Qi

Published in: Annals of Data Science | Issue 2/2014

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Abstract

Twin support vector machine (TWSVM), an useful extension of the traditional SVM, becomes the current researching hot spot in machine learning during the last few years. For the binary classification problem, the basic idea of TWSVM is to seek two nonparallel proximal hyperplanes such that each hyperplane is closer to one of the two classes and is at least one distance from the other. TWSVM has lower computational complexity and better generalization ability, therefore in the last few years it has been studied extensively and developed rapidly. Considering the many variants of TWSVM, a systematic survey is needed and helpful to understand and use this family of data mining techniques more easily. The purpose of this paper is to closely review TWSVMs and provide an insightful understanding of current developments, at the same time point out their limitations and highlight the major opportunities and challenges, as well as potential important research directions.
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Metadata
Title
Review on: Twin Support Vector Machines
Authors
Yingjie Tian
Zhiquan Qi
Publication date
01-06-2014
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 2/2014
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-014-0018-4

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