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Neural Computing and Applications

Erschienen in:

01.04.2013 | Original Article

Robust minimum class variance twin support vector machine classifier

verfasst von: Xinjun Peng, Dong Xu

Erschienen in: Neural Computing and Applications | Ausgabe 5/2013

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Abstract

The recently proposed twin support vector machine (TWSVM) obtains much faster training speed and comparable performance than classical support vector machine. However, it only considers the empirical risk minimization principle, which leads to poor generalization for real-world applications. In this paper, we formulate a robust minimum class variance twin support vector machine (RMCV-TWSVM). RMCV-TWSVM effectively overcomes the shortcoming in TWSVM by introducing a pair of uncertain class variance matrices in its objective functions. As a special case, we present a special type of the uncertain class variance matrices by combining the empirical positive and negative class variance matrices. Computational results on several synthetic as well as benchmark datasets indicate the significant advantages of proposed classifier in both computational time and test accuracy.

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Titel: Robust minimum class variance twin support vector machine classifier
verfasst von: Xinjun Peng
Dong Xu
Publikationsdatum: 01.04.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 5/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0791-3

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