Smooth Newton method for implicit Lagrangian twin support vector regression

Article type: Research Article

Authors: Balasundaram, S.^{; *} | Tanveer, M.

Affiliations: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

Correspondence: [*] Corresponding author: S. Balasundaram, School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India. E-mail: [email protected]

Abstract: A new smoothing approach for the implicit Lagrangian twin support vector regression is proposed in this paper. Our formulation leads to solving a pair of unconstrained quadratic programming problems of smaller size than in the classical support vector regression and their solutions are obtained using Newton-Armijo algorithm. This approach has the advantage that a system of linear equations is solved in each iteration of the algorithm. Numerical experiments on several synthetic and real-world datasets are performed and, their results and training time are compared with both the support vector regression and twin support vector regression to verify the effectiveness of the proposed method.

Keywords: Implicit Lagrangian support vector machines, nonparallel planes, support vector regression, smoothing technique, twin support vector regression

DOI: 10.3233/KES-130277

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 17, no. 4, pp. 267-278, 2013