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论文类型：会议论文

发表时间：2011-04-15

收录刊物：EI、Scopus

页面范围：1147-1150

摘要：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.