Learning with Sequential Minimal Transductive Support Vector Machine

Authors:
Xinjun Peng;Yifei Wang
Affiliations:
Department of Mathematics, Shanghai Normal University, P.R. China 200234 and Scientific Computing Key Laboratory of Shanghai Universities, P.R. China 200234;Department of Mathematics, Shanghai University, P.R. China 200444
Venue:
FAW '09 Proceedings of the 3d International Workshop on Frontiers in Algorithmics
Year:
2009

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Abstract

While transductive support vector machine (TSVM) utilizes the information carried by the unlabeled samples for classification and acquires better classification performance than support vector machine (SVM), the number of positive samples must be appointed before training and it is not changed during the training phase. In this paper, a sequential minimal transductive support vector machine (SMTSVM) is discussed to overcome the deficiency in TSVM. It solves the problem of estimation the penalty value after changing a temporary label by introducing the sequential minimal way. The experimental results show that SMTSVM is very promising.