A new approach for training Lagrangian twin support vector machine via unconstrained convex minimization
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- 作者：S. Balasundaram ; Deepak Gupta ; Subhash Chandra Prasad
- 关键词：Generalized Hessian approach ; Smooth approximation formulation ; Twin support vector machine
- 刊名：Applied Intelligence
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：46
- 期：1
- 页码：124-134
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools;
- 出版者：Springer US
- ISSN：1573-7497
- 卷排序：46
文摘
In this paper, a novel unconstrained convex minimization problem formulation for the Lagrangian dual of the recently introduced twin support vector machine (TWSVM) in simpler form is proposed for constructing binary classifiers. Since the objective functions of the modified minimization problems contain non-smooth ‘plus’ function, we solve them by Newton iterative method either by considering their generalized Hessian matrices or replacing the ‘plus’ function by a smooth approximation function. Numerical experiments were performed on a number of interesting real-world benchmark data sets. Computational results clearly illustrates the effectiveness and the applicability of the proposed approach as comparable or better generalization performance with faster learning speed is obtained in comparison with SVM, least squares TWSVM (LS-TWSVM) and TWSVM.