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2017 | OriginalPaper | Chapter

5. Variants of Twin Support Vector Machines: Some More Formulations

Authors : Jayadeva, Reshma Khemchandani, Suresh Chandra

Published in: Twin Support Vector Machines

Publisher: Springer International Publishing

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Abstract

Twin support vector machine formulation (TWSVM) is based on the idea of generalized eigenvalue proximal support vector machine formulation (GEPSVM), which determines two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is much smaller than that of the 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 chapter mainly reviews the research progress of TWSVM and presents some of its extensions including the learning model and specific applications in recent years.

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previous chapter TWSVR: Twin Support Vector Machine Based Regression
next chapter TWSVM for Unsupervised and Semi-supervised Learning
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Metadata
Title
Variants of Twin Support Vector Machines: Some More Formulations
Authors
Jayadeva
Reshma Khemchandani
Suresh Chandra
Copyright Year
2017
Book
Twin Support Vector Machines

Print ISBN: 978-3-319-46184-7

Electronic ISBN: 978-3-319-46186-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-46186-1_5

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