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http://dx.doi.org/10.5391/JKIIS.2010.20.3.375

Design of Robust Support Vector Machine Using Genetic Algorithm  

Lee, Hee-Sung (연세대학교 전기전자공학부)
Hong, Sung-Jun (연세대학교 전기전자공학부)
Lee, Byung-Yun (연세대학교 전기전자공학부)
Kim, Eun-Tai (연세대학교 전기전자공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.20, no.3, 2010 , pp. 375-379 More about this Journal
Abstract
The support vector machine (SVM) has been widely used in variety pattern recognition problems applicable to recommendation systems due to its strong theoretical foundation and excellent empirical successes. However, SVM is sensitive to the presence of outliers since outlier points can have the largest margin loss and play a critical role in determining the decision hyperplane. For robust SVM, we limit the maximum value of margin loss which includes the non-convex optimization problem. Therefore, we proposed the design method of robust SVM using genetic algorithm (GA) which can solve the non-convex optimization problem. To demonstrate the performance of the proposed method, we perform experiments on various databases selected in UCI repository.
Keywords
SVM; Robust SVM; GA; outlier; UCI repository;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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