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2017 | OriginalPaper | Chapter

3. Twin Support Vector Machines (TWSVM) for Classification

Authors : Jayadeva, Reshma Khemchandani, Suresh Chandra

Published in: Twin Support Vector Machines

Publisher: Springer International Publishing

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Abstract

This chapter constitute the core around which the latter chapters are developed. Here the first and most basic formulation of the Twin Support Vector Machine (TWSVM) is presented. Certain advantages and some of the possible disadvantages/computational issues are noted. Two variants of TWSVM namely the Twin Bounded Support Vector Machine (TBSVM) and then Improved Twin Support Vector Machine (ITSVM), are discussed. These two variants attempt to handle some of the issues related with the original TWSVM formulation.

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previous chapter Generalized Eigenvalue Proximal Support Vector Machines
next chapter TWSVR: Twin Support Vector Machine Based Regression
Literature
1.
Mangasarian, O. L., & Wild, E. W. (2006). Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(1), 69–74.CrossRef
2.
Jayadeva, Khemchandani, R., & Chandra, S. (2007). Twin support vector machines for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(5), 905–910.
3.
Shao, Y.-H., Zhang, C.-H., Wang, X.-B., & Deng, N.-Y. (2011). Improvements on twin support vector machines. IEEE Transactions on Neural Networks, 22(6), 962–968.CrossRef
4.
Tian, Y. J., Ju, X. C., Qi, Z. Q., & Shi, Y. (2013). Improved twin support vector machine. Science China Mathematics, 57(2), 417–432.MathSciNetCrossRefMATH
5.
6.
Chandra, S., Jayadeva, & Mehra, A. (2009). Numerical optimization with applications. New Delhi: Narosa Publishing House.
7.
Saunders, C., Gammerman, A., & Vovk, V. (1998). Ridge regression learning algorithm in dual variables. In Proceedings of the Fifteenth International Conference on Machine Learning (pp. 515–521).
8.
Fung, G., & Mangasarian, O. L. (2001). Proximal support vector machine classifiers. In F. Provost & R. Srikant (Eds.), Proceedings of Seventh International Conference on Knowledge Discovery and Data Mining (pp. 77–86).
9.
Peng, X. J., Xu, D., Kong, L., & Chen, D. (2016). L1-norm loss based twin support vector machine for data recognition. Information Sciences, 340–341, 86–103.
10.
Musicant, D. R. (1998). NDC: Normally Distributed Clustered Datasets, Computer Sciences Department, University of Wisconsin, Madison.
Metadata
Title
Twin Support Vector Machines (TWSVM) for Classification
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_3

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