Journal
PATTERN RECOGNITION LETTERS
Volume 29, Issue 13, Pages 1842-1848Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2008.05.016
Keywords
support vector machines; pattern recognition; twin support vector machines
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This paper enhances the recently proposed twin SVM Jayadeva et al. [Jayadeva, Khemchandani, R., Chandra, S., 2007. Twin support vector machines for pattern classification. IEEE Trans. Pattern Anal. Machine Intell. 29 (5), 905-910] using smoothing techniques to smooth twin SVM for binary classification. We attempt to solve the primal quadratic programming problems of twin SVM by converting them into smooth unconstrained minimization problems. The smooth reformulations are solved using the well-known Newton-Armijo algorithm. The effectiveness of the enhanced method is demonstrated by experimental results on available benchmark datasets. (C) 2008 Elsevier B.V. All rights reserved.
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