Application of smoothing technique on twin support vector machines

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.