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Neural Computing and Applications

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01.04.2014 | Original Article

Twin support vector hypersphere (TSVH) classifier for pattern recognition

verfasst von: Xinjun Peng, Dong Xu

Erschienen in: Neural Computing and Applications | Ausgabe 5/2014

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Abstract

Motivated by the support vector data description, a classical one-class support vector machine, and the twin support vector machine classifier, this paper formulates a twin support vector hypersphere (TSVH) classifier, a novel binary support vector machine (SVM) classifier that determines a pair of hyperspheres by solving two related SVM-type quadratic programming problems, each of which is smaller than that of a conventional SVM, which means that this TSVH is more efficient than the classical SVM. In addition, the TSVH successfully avoids matrix inversion compared with the twin support vector machine, which indicates learning algorithms of the SVM can be easily extended to this TSVH. Computational results on several synthetic as well as benchmark data sets indicate that the proposed TSVH is not only faster, but also obtains better generalization.

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If \({\sum\nolimits_{i\in {\mathcal{I}}^+}\user2{z}_{i}\user2{z}^T_{i}}\) and \({\sum\nolimits_{j\in {\mathcal{I}}^-}\user2{z}_{j}\user2{z}^T_{j}}\) are ill-conditioning, we can add the regularization terms \(\epsilon_iE,\,\epsilon_i>0, \) here, E is an identity matrix of appropriate dimension. Remark that these two added regularization terms are equivalent to adding two regularization terms \(\frac{\epsilon_i}{2}||(\user2{w}_\pm||^2+b_\pm^2),i=1,2, \) into the objective functions of TSVM, which make TWSVMs be more theoretical sound [37, 38]. In the view point of regression, they make the nonparallel hyperplanes be more smooth and robust.

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Titel: Twin support vector hypersphere (TSVH) classifier for pattern recognition
verfasst von: Xinjun Peng
Dong Xu
Publikationsdatum: 01.04.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 5/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1306-6

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