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http://dx.doi.org/10.5391/JKIIS.2003.13.3.266

Support Vector Learning for Abnormality Detection Problems  

Park, Joo-Young (고려대학교 서창캠퍼스 제어계측공학과)
Leem, Chae-Hwan (LG 전자)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.13, no.3, 2003 , pp. 266-274 More about this Journal
Abstract
This paper considers an incremental support vector learning for the abnormality detection problems. One of the most well-known support vector learning methods for abnormality detection is the so-called SVDD(support vector data description), which seeks the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to modify the SVDD into the direction of utilizing the relation between the optimal solution and incrementally given training data. After a thorough review about the original SVDD method, this paper establishes an incremental method for finding the optimal solution based on certain observations on the Lagrange dual problems. The applicability of the presented incremental method is illustrated via a design example.
Keywords
SVDD;
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