Presentation | 1999/12/16 Warp-Robust Classification Function Learning Based on Linear Programming and Dynamic Programming Tomtaka HIROSE, Yosinori KATAYAMA, Hiroaki SAKOE, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | A new linear classification function learning algorithm is described for warp-robust pattern recognition. The algorithm involves learning cycle with linear programming based classification function calculation and dynamic programming based pattern warping. At each learning cycle, each of training patterns is warped so that resultant pattern is most inseparable with current classification function, and is used in the further learning cycles. The algorithm validity is established through a learning experiment. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | classification function / warp-robust learning / linear programming / dynamic programming |
Paper # | PRMU99-169 |
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Committee | PRMU |
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Conference Date | 1999/12/16(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) | Warp-Robust Classification Function Learning Based on Linear Programming and Dynamic Programming |
Sub Title (in English) | |
Keyword(1) | classification function |
Keyword(2) | warp-robust learning |
Keyword(3) | linear programming |
Keyword(4) | dynamic programming |
1st Author's Name | Tomtaka HIROSE |
1st Author's Affiliation | MITSUBISHI ELECTRIC() |
2nd Author's Name | Yosinori KATAYAMA |
2nd Author's Affiliation | Department of Intelligent Systems, Kyushu University |
3rd Author's Name | Hiroaki SAKOE |
3rd Author's Affiliation | Department of Intelligent Systems, Kyushu University |
Date | 1999/12/16 |
Paper # | PRMU99-169 |
Volume (vol) | vol.99 |
Number (no) | 514 |
Page | pp.pp.- |
#Pages | 8 |
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