The Research on Semi-Supervised Support Vector Data Description Multi-Classification Algorithm

Abstract:

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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.

Info:

Periodical:

Advanced Materials Research (Volumes 268-270)

Edited by:

Feng Xiong

Pages:

1115-1120

DOI:

10.4028/www.scientific.net/AMR.268-270.1115

Citation:

D. Q. Xue "The Research on Semi-Supervised Support Vector Data Description Multi-Classification Algorithm", Advanced Materials Research, Vols. 268-270, pp. 1115-1120, 2011

Online since:

July 2011

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Price:

$35.00

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