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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 11/2017

02.03.2016 | Review

Twin support vector machine: theory, algorithm and applications

verfasst von: Shifei Ding, Nan Zhang, Xiekai Zhang, Fulin Wu

Erschienen in: Neural Computing and Applications | Ausgabe 11/2017

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Abstract

Twin support vector machine (TWSVM) has gained increasing interest from various research fields recently. In this paper, we aim to report the current state of the theoretical research and practical advances on TWSVM. We first give the basic thought and theory of TWSVM, including the theory of proximal support vector machine, generalized eigenvalue proximal support vector machine, and TWSVM. Then, we focus on the various improvements made to TWSVM, mainly including least squares twin support vector machine, smooth twin support vector machine, regularized twin support vector machine, projection twin support vector machine, and modified TWSVM on the model selection problem. These newly emerging algorithms greatly expand the applications of TWSVM. In recent years, there is a lot of research on application of TWSVM. Next, we list some research on application of TWSVM in detail. Finally, we try to provide a comprehensive view of these advances in TWSVM and discuss the direction of future research.
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Metadaten
Titel
Twin support vector machine: theory, algorithm and applications
verfasst von
Shifei Ding
Nan Zhang
Xiekai Zhang
Fulin Wu
Publikationsdatum
02.03.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2245-4

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