| Presentation | 2004/9/4 Kernel Methods and their Application for Image Understanding Takio Kurita, Kenji Nishida, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Support vector machine (SVM) has been extended to build up nonlinear classifier using the kernel trick. It is recognized as one of the best models for two class classification among the many methods currently known because it is devised to obtain high performance for unlearned data. This paper reviews kernel methods centering on the SVM and introduces some examples of applications for image understanding. |
| Keyword(in Japanese) | (See Japanese page) |
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| Paper # | PRMU2004-78 |
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| Conference Information | |
| Committee | PRMU |
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| Conference Date | 2004/9/4(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Pattern Recognition and Media Understanding (PRMU) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Kernel Methods and their Application for Image Understanding |
| Sub Title (in English) | |
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| 1st Author's Name | Takio Kurita |
| 1st Author's Affiliation | Neurosceince Research Institute, National Institute of Advanced Indastrial Science and Technology() |
| 2nd Author's Name | Kenji Nishida |
| 2nd Author's Affiliation | Neurosceince Research Institute, National Institute of Advanced Indastrial Science and Technology |
| Date | 2004/9/4 |
| Paper # | PRMU2004-78 |
| Volume (vol) | vol.104 |
| Number (no) | 291 |
| Page | pp.pp.- |
| #Pages | 8 |
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