Presentation | 2007-11-19 Midpoint-Validation Method for Support Vector Machine Shota IKUBO, Hiroki TAMURA, Koichi TANNO, |
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Abstract(in English) | In recent years, support vector machine (abbr. SVM) is one of the most influential and powerful tools for solving classification. The most attractive notion of SVM is the idea of the large margin. However, many experiment results showed that the boundary line created by SVM has deviation. Therefore, SVM uses the cross-validation technique in many cases. In this paper, we propose the method of decreasing the deviation of SVM by creating a midpoint data. The proposed method creates midpoint data and adjusts parameter of SVM by midpoint data. We compare its performance with those of the original SVM, Multilayer Perceptron (abbr. MLP), Radial Basis Function Neural Network (abbr. RBF), and tested our proposed method on several 2-class pattern classification problems. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Support Vector Machine / Neural Network / Pattern Classification Problem |
Paper # | NC2007-65 |
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Committee | NC |
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Conference Date | 2007/11/11(1days) |
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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) | Midpoint-Validation Method for Support Vector Machine |
Sub Title (in English) | |
Keyword(1) | Support Vector Machine |
Keyword(2) | Neural Network |
Keyword(3) | Pattern Classification Problem |
1st Author's Name | Shota IKUBO |
1st Author's Affiliation | Faculty of Engineering, University of MIYAZAKI() |
2nd Author's Name | Hiroki TAMURA |
2nd Author's Affiliation | Faculty of Engineering, University of MIYAZAKI |
3rd Author's Name | Koichi TANNO |
3rd Author's Affiliation | Faculty of Engineering, University of MIYAZAKI |
Date | 2007-11-19 |
Paper # | NC2007-65 |
Volume (vol) | vol.107 |
Number (no) | 328 |
Page | pp.pp.- |
#Pages | 4 |
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