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发表刊物：INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING: BIG DATA AND MACHINE LEARNING TECHNIQUES, ISCIDE 2015, PT II

关键字：Machine learningSemi-supervised learningGSSLE

摘要：This paper presents a graph based semi-supervised learning algorithm on evolutionary data. By applying evolutionary smoothness assumption and incorporating it to the general framework of graph-based semi-supervised learning, we got a new algorithm called GSSLE. Empirical evaluations show that our method outperforms other state-of-the-art methods in terms of stability. It is able to deal with dynamic feature space tasks and proves efficient even if we do not have many unlabeled samples in the semi-supervised procedure.

合写作者：Yifei Yang, Changshui Zhang,Tiejun Huang, Cliff Reader, Weibei Dou,Xilin Chen

第一作者：Yanglei Song,Wen Gao

论文类型：会议论文

通讯作者：Changshui Zhang,Wen Gao,Wen Gao

ISSN号：0302-9743

是否译文：否

发表时间：2015-01-01