Author:

Keywords:

SISTA, Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Support vector machine, Pinball loss, Sequential minimal optimization, SMO ALGORITHM, SUPPORT, CONVERGENCE, CLASSIFIER, 08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences, Artificial Intelligence & Image Processing, 40 Engineering, 46 Information and computing sciences, 52 Psychology

Abstract:

© 2014 Elsevier B.V. To pursue the insensitivity to feature noise and the stability to re-sampling, a new type of support vector machine (SVM) has been established via replacing the hinge loss in the classical SVM by the pinball loss and was hence called a pin-SVM. Though a different loss function is used, pin-SVM has a similar structure as the classical SVM. Specifically, the dual problem of pin-SVM is a quadratic programming problem with box constraints, for which the sequential minimal optimization (SMO) technique is applicable. In this paper, we establish SMO algorithms for pin-SVM and its sparse version. The numerical experiments on real-life data sets illustrate both the good performance of pin-SVMs and the effectiveness of the established SMO methods.