关键词: 半监督学习, 支持向量机, 直推式学习, 超球结构

Abstract: Progressive Transductive Support Vector Machines（PTSVM） has some drawbacks such as slower training speed，and unstable learning performance，a Progressive Transductive Support Vector Machines learning algorithm based on hypersphere structure is proposed.Labeled data of each class are described by using Support Vector Domain Description（SVDD） and the corresponding smallest enclosing hypersphere is obtained.Then，the method selects new unlabeled samples located near the boundary of the hypersphere.Finally，labeled data available is used to train standard SVM.Experiment results show the method can improve greatly the computing speed.Moreover，it can keep，in fact sometimes improve，the classification accuracy in general.

Key words: semi-supervised learning, Support Vector Machines（SVM）, transductive learning, hypersphere structure