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• W62865002 abstract "The parametric-margin @n-support vector machine (par-@n-SVM) is a useful classifier in many cases, especially when the noise is heteroscedastic. In this paper, the geometric interpretation for the par-@n-SVM is described, which is equivalent to finding a couple of points in two disjoint @m-reduced convex hulls (@m-RCHs) by simultaneously minimizing the square distance and maximizing the square norm of their sum with a weight factor 1/(c@n) given by users. Motivated by the Gilbert-Schlesinger-Kozinec (GSK) and Mitchell-Dem'yanov-Malozemov (MDM) algorithms, two geometric algorithms, called the parametric @m-GSK(par-@m-GSK) and parametric @m-MDM(par-@m-MDM) algorithms, are introduced to solve the par-@n-SVM. Computational results on several synthetic as well as benchmark datasets demonstrate the significant performance of the proposed algorithms in terms of both kernel operations and classification accuracy." @default.
• W62865002 created "2016-06-24" @default.
• W62865002 creator A5002008253 @default.
• W62865002 creator A5031353058 @default.
• W62865002 date "2013-01-01" @default.
• W62865002 modified "2023-09-27" @default.
• W62865002 title "Geometric algorithms for parametric-margin vector machine" @default.
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• W62865002 doi "https://doi.org/10.1016/j.neucom.2012.06.026" @default.
• W62865002 hasPublicationYear "2013" @default.
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