Title :
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
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;
Conference_Titel :
Internet Computing & Information Services (ICICIS), 2011 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4577-1561-7
DOI :
10.1109/ICICIS.2011.152
