Presentation | 2004/1/19 Maximal Margin Classifier based on Geometric Methods (Neurocomputing) Manabu MUKAIYAMA, Haruhisa TAKAHASHI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | The support vector machine provides good learning performance by maximizing the margin and use of the Kernel Method. To solve a quadratic programing for maximizing the margin requires the computational efforts of polynomial order O(l^3) for sample size l. Although several implementaion methods of SVM such as SMO are proposed, the computational efforts are not yet realistic for large sample size. In this report, we propose a geometric method which constracts a maximal margin hyperplane finding support vectors iteratively starting from the vector pair minimizing the distance between classes. The computational complexity of this method is O(l^2) or O(m^3), m being the number of support vectors. Computer experiments shows that our method is advantageous when the number of support vectors is relatively small. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Pattern Recognition / Maximal Margin / Low Computational Effort / SVM / SMO |
Paper # | NC2003-114 |
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Committee | NC |
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Conference Date | 2004/1/19(1days) |
Place (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) | Maximal Margin Classifier based on Geometric Methods (Neurocomputing) |
Sub Title (in English) | |
Keyword(1) | Pattern Recognition |
Keyword(2) | Maximal Margin |
Keyword(3) | Low Computational Effort |
Keyword(4) | SVM |
Keyword(5) | SMO |
1st Author's Name | Manabu MUKAIYAMA |
1st Author's Affiliation | Graduate School of Electro-Communications, The University of Electro-Communications() |
2nd Author's Name | Haruhisa TAKAHASHI |
2nd Author's Affiliation | The University of Electro-Communications |
Date | 2004/1/19 |
Paper # | NC2003-114 |
Volume (vol) | vol.103 |
Number (no) | 601 |
Page | pp.pp.- |
#Pages | 6 |
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