Presentation | 2002/7/19 Support Vector Machines and Multi-objective Programming Takeshi ASADA, Hirotaka NAKAYAMA, |
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Abstract(in Japanese) | (See Japanese page) |
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 |
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Conference Date | 2002/7/19(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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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 |
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