| Presentation | 2008-09-05 Kernel Method : Fundamentals and Applications Eisaku MAEDA, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Kernel methods is a class of well known techniques for pattern analysis algorithms such as Support Vector Machines (SVM), and enable to operate non-linear processing in a transformed high dimensional feature space without computing the coordinates of the data. Today, kernel methods are not only combined with a lot of linear methods for vector data, but also extended to methods for structured data such as strings, trees and graphs, and are widely applied to computer vision, natural language processing, bioinformatics, and data mining, etc. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | |
| Paper # | PRMU2008-56,HIP2008-56 |
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| Conference Information | |
| Committee | PRMU |
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| Conference Date | 2008/8/29(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| 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 Method : Fundamentals and Applications |
| Sub Title (in English) | |
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| 1st Author's Name | Eisaku MAEDA |
| 1st Author's Affiliation | NTT Communication Science Laboratories() |
| Date | 2008-09-05 |
| Paper # | PRMU2008-56,HIP2008-56 |
| Volume (vol) | vol.108 |
| Number (no) | 198 |
| Page | pp.pp.- |
| #Pages | 51 |
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