Indexed by:会议论文

Date of Publication:2011-04-15

Included Journals:EI、Scopus

Page Number:1147-1150

Abstract:Semi-supervised Support Vector Machines is an appealing method for using unlabeled data in classification. Based on a smooth approximation function named as aggregate function, a global aggregate homotopy method is presented in this paper. Compared to some existing algorithms, the new method is superior in no need of introducing extra variables or solving a sequence of subproblems. Moreover, the global convergence can make better local minima and then result in better prediction accuracy. Final numerical results reveals the efficiency of the method. ? 2011 IEEE.