A New Conic Approach to Semisupervised Support Vector Machines. (17th March 2016)

• Record Type: Journal Article
• Authors: Tian, Ye; Luo, Jian; Yan, Xin
• Other Names: Ponsart Jean-Christophe Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2016(2016)
• Publication Date: 2016-03-17
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2016/6471672
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : We propose a completely positive programming reformulation of the 2-norm soft marginS 3 V M model. Then, we construct a sequence of computable cones of nonnegative quadratic forms over a union of second-order cones to approximate the underlying completely positive cone. Anϵ -optimal solution can be found in finite iterations using semidefinite programming techniques by our method. Moreover, in order to obtain a good lower bound efficiently, an adaptive scheme is adopted in our approximation algorithm. The numerical results show that the proposed algorithm can achieve more accurate classifications than other well-known conic relaxations of semisupervised support vector machine models in the literature.