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

Author

Xiantong, Huang ; Songjuan, Zhang

Author_Institution

Dept. of Comput. Sci. & Technol., Nanyang Inst. of Technol., Nanyang, China

fYear

2011

fDate

17-18 Sept. 2011

Firstpage

575

Lastpage

578

Abstract

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 semi-supervised 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 non-target 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.

Keywords

data analysis; learning (artificial intelligence); pattern classification; support vector machines; S3VDD-MC algorithm; information distribution; k-classification problem; membership degree; semisupervised multiclassification learning algorithm; support vector data description; Classification algorithms; Machine learning; Machine learning algorithms; Signal processing algorithms; Software algorithms; Support vector machines; Training; Multiclassification; Statistical Learning Theory; Support Vector Data Description; Support Vector Machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Internet Computing & Information Services (ICICIS), 2011 International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4577-1561-7

Type

conf

DOI

10.1109/ICICIS.2011.152

Filename

6063330