Presentation 2002/7/19
Support Vector Machines and Multi-objective Programming
Takeshi ASADA, Hirotaka NAKAYAMA,
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Abstract(in English) Support Vector Machines(SVMs) are now thought as a powerful method for solving pattern recognition problems. SVMs are usually formulated as Quadratic Programming(QP). Using another distance function, SVMs can be formulated as Linear Programming(LP). In general, SVMs tend to make overlearning. In order to overcome this difficulty, the notion of soft margin is introduced. In this event, it is difficult to decide the weight for slack variables reflecting soft margin. In this paper, soft margin method is extended to Multi Objective Linear Programming(MOLP). It will be shown throughout several examples that SVMs reformulated as MOLP can give a good performance in pattern classification.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Support vector machines / Multi-objective programming / Linear programming / Additional learning
Paper # NC2002-32
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Committee NC
Conference Date 2002/7/19(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Support Vector Machines and Multi-objective Programming
Sub Title (in English)
Keyword(1) Support vector machines
Keyword(2) Multi-objective programming
Keyword(3) Linear programming
Keyword(4) Additional learning
1st Author's Name Takeshi ASADA
1st Author's Affiliation Graduate School of Natural Sciences, Konan University()
2nd Author's Name Hirotaka NAKAYAMA
2nd Author's Affiliation Department of Information Science and Systems Engineering, Konan University
Date 2002/7/19
Paper # NC2002-32
Volume (vol) vol.102
Number (no) 253
Page pp.pp.-
#Pages 6
Date of Issue