On implicit Lagrangian twin support vector regression by Newton method
Sadhu Ramakrishnan Balasundaram, Deepak Gupta · International Journal of Computational Intelligence Systems · 2013
In this work, an implicit Lagrangian for the dual twin support vector regression is proposed.Our formulation leads to determining non-parallel ε -insensitive down-and up-bound functions for the unknown regressor by constructing two unconstrained quadratic programming problems of smaller size, instead of a single large one as in the standard support vector regression (SVR).The two related support vector machine type problems are solved using Newton method.Numerical experiments were performed on a number of interesting synthetic and real-world benchmark datasets and their results were compared with SVR and twin SVR.Similar or better generalization performance of the proposed method clearly illustrates its effectiveness and applicability.