A v-twin support vector machine (v-TSVM) classifier and its geometric algorithms

作者：Peng Xinjun^*

来源：Information Sciences, 2010, 180(20): 3863-3875.

DOI：10.1016/j.ins.2010.06.039

• Pattern recognition
• Twin support vector machine
• Geometric interpretation
• Geometric algorithm
• Probabilistic speed-up strategy

In this paper, a v-twin support vector machine (v-TSVM) is presented, improving upon the recently proposed twin support vector machine (TSVM). This v-TSVM introduces a pair of parameters (v) to control the bounds of the fractions of the support vectors and the error margins. The theoretical analysis shows that this v-TSVM can be interpreted as a pair of minimum generalized Mahalanobis-norm problems on two reduced convex hulls (RCHs). Based on the well-known Gilbert's algorithm, a geometric algorithm for TSVM (GA-TSVM) and its probabilistic speed-up version, named PGA-TSVM, are presented. Computational results on several synthetic as well as benchmark datasets demonstrate the significant advantages of the proposed algorithms in terms of both computation complexity and classification accuracy.

• 出版日期2010-10-15
• 单位上海师范大学; 上海大学