会议专题

Semi-supervised Support Vector Data Description Multi-classification Learning Algorithm

Semi-supervised Support Vector Data Description multi-classification algorithm is presented,in order to solve less labeled data learning,difficulties in the implementation and poor results of semisupervised multi-classification,which full use the distribution of information in of non- target samples.S3VDD-MC algorithm defines the degree of membership of non-target samples,in order to get the nontarget samples accepted labels or refused labels,on this basis,several super-spheres constructed,a k-classification problem is transformed into k SVDDs problem.Finally,the simulation results verify the effectiveness of the algorithm.

Statistical Learning Theory Support Vector Machines Support Vector Data Description Multiclassification

Huang Xiantong Zhang Songjuan

Department of Computer Science and Technology Nanyang Institute of Technology Nanyang,China

国际会议

2010 4th International Conference on Intelligent Information Techonlogy Application(第四届智能信息技术应用国际学术研讨会 IITA 2010)

秦皇岛

英文

95-98

2010-11-05(万方平台首次上网日期,不代表论文的发表时间)