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| Presentation | 2010-12-09 16:40 Large Geometric Margin Minimum Classification Error Training for Kernel-based High Dimensional Space Hideyuki Watanabe (NICT), Shigeru Katagiri, Mamoru Adachi, Miho Ohsaki (Doshisha Univ.)  PRMU2010-136 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Large Geometric Margin Minimum Classification Error (LGM-MCE) training has been successfully applied to multi-class classification tasks. It directly aims at the status of the minimum classification error probability while increasing the geometric margin, the distance between the classification boundary and its nearest training sample. In this paper we introduce the concept of kernels to cope with the complicated distributions of patterns and propose a new training method that applies LGM-MCE formalization to linear discriminant functions defined in high-dimensional space to which input patterns are implicitly mapped by the kernels. We also show the relationship between our proposed method and several well-studied classifiers, such as a Support Vector Machine classifier, a Gaussian Mixture Model classifier, and a Radial Basis Function network. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | geometric margin / Minimum Classification Error / kernel / Support Vector Machine / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 110, no. 330, PRMU2010-136, pp. 55-60, Dec. 2010. | |
| Paper # | PRMU2010-136 | |
| Date of Issue | 2010-12-02 (PRMU) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | PRMU2010-136 |
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| Conference Information | |
| Committee | PRMU FM |
| Conference Date | 2010-12-09 - 2010-12-10 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2010-12-PRMU |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Large Geometric Margin Minimum Classification Error Training for Kernel-based High Dimensional Space |
| Sub Title (in English) | |
| Keyword(1) | geometric margin |
| Keyword(2) | Minimum Classification Error |
| Keyword(3) | kernel |
| Keyword(4) | Support Vector Machine |
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| 1st Author's Name | Hideyuki Watanabe |
| 1st Author's Affiliation | National Institute of Information and Communications Technology (NICT) |
| 2nd Author's Name | Shigeru Katagiri |
| 2nd Author's Affiliation | Graduate School, Doshisha University (Doshisha Univ.) |
| 3rd Author's Name | Mamoru Adachi |
| 3rd Author's Affiliation | Graduate School, Doshisha University (Doshisha Univ.) |
| 4th Author's Name | Miho Ohsaki |
| 4th Author's Affiliation | Graduate School, Doshisha University (Doshisha Univ.) |
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| Speaker | Author-1 |
| Date Time | 2010-12-09 16:40:00 |
| Presentation Time | 30 minutes |
| Registration for | PRMU |
| Paper # | PRMU2010-136 |
| Volume (vol) | vol.110 |
| Number (no) | no.330 |
| Page | pp.55-60 |
| #Pages | 6 |
| Date of Issue | 2010-12-02 (PRMU) |
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