In this paper, we propose a novel large margin proximal non-parallel twin support vector machine for binary classification. The significant advantages over twin support vector machine are that the structural risk minimization principle is implemented and by adopting uncommon constraint formulation for the primal problem, the proposed method avoids the computation of the large inverse matrices before training which is inevitable in the formulation of twin support vector machine. In addition, the dual coordinate descend algorithm is used to solve the optimization problems to accelerate the training efficiency. Experimental results exhibit the effectiveness and the classification accuracy of the proposed method.
CITATION STYLE
Liu, M., & Shao, Y. (2018). Large Margin Proximal Non-parallel Support Vector Classifiers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10862 LNCS, pp. 715–721). Springer Verlag. https://doi.org/10.1007/978-3-319-93713-7_69
Mendeley helps you to discover research relevant for your work.