期刊名称：Journal of Data Analysis and Information Processing
印刷版ISSN：2327-7211
电子版ISSN：2327-7203
出版年度：2013
卷号：1
期号：1
页码：1-7
DOI：10.4236/jdaip.2013.11001
出版社：Scientific Research Publishing
摘要：Semi-supervised Support Vector Machines is an appealing method for using unlabeled data in classification. Smoothing homotopy method is one of feasible method for solving semi-supervised support vector machines. In this paper, an inexact implementation of the smoothing homotopy method is considered. The numerical implementation is based on a truncated smoothing technique. By the new technique, many “non-active” data can be filtered during the computation of every iteration so that the computation cost is reduced greatly. Besides this, the global convergence can make better local minima and then result in lower test errors. Final numerical results verify the efficiency of the method.
关键词：Semi-Supervised Classification; Support Vector Machines; Truncated Smoothing Technique; Global Convergence
