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| Presentation | 2007-12-13 11:40 [Special Talk] (not registered) Shigeo Abe (Kobe Univ.)  PRMU2007-139 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Support vector machines (SVM) have been attracting much attention because of their high generalization ability for a wide range of applications. However, because SVMs are originally defined for two-class classification problems, an extension to multiclass problems is not unique and in some extensions unclassifiable regions exist. There are roughly four methods for multiclass problems: (1) one-against-all, (2) pairwise, (3) ECOC (error correcting output code), and (4) all-at-once SVMs. For (1)-(3), unclassifiable regions exist. Another problem is that the SVM is a black box. In this talk, I discuss some approaches to solve these problems fusing SVMs with fuzzy systems. To solve the problem of unclassifiable regions, we propose fuzzy SVMs, in which we introduce membership functions that are orthogonal to the decision functions and define the membership of data for each class. Then I discuss the advantages and disadvantages of these SVMs based on theoretical analysis combined with performance evaluation for some benchmark data sets. As approaches to solve the problem of the black box problem, I talk fuzzy classifiers based on the kernel Mahalanobis distances and kernel discriminant analysis. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | kernel method / kernel Mahalanobis distance / kernel discriminant analysis / support vector machine / multiclass problem / fuzzy system / pattern classification / | |
| Reference Info. | IEICE Tech. Rep., vol. 107, no. 384, PRMU2007-139, pp. 25-25, Dec. 2007. | |
| Paper # | PRMU2007-139 | |
| Date of Issue | 2007-12-06 (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 | PRMU2007-139 |
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| Conference Information | |
| Committee | PRMU |
| Conference Date | 2007-12-13 - 2007-12-14 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kobe Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2007-12-PRMU |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | (not registered) |
| Sub Title (in English) | |
| Keyword(1) | kernel method |
| Keyword(2) | kernel Mahalanobis distance |
| Keyword(3) | kernel discriminant analysis |
| Keyword(4) | support vector machine |
| Keyword(5) | multiclass problem |
| Keyword(6) | fuzzy system |
| Keyword(7) | pattern classification |
| Keyword(8) | |
| 1st Author's Name | Shigeo Abe |
| 1st Author's Affiliation | Kobe University (Kobe Univ.) |
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| Speaker | Author-1 |
| Date Time | 2007-12-13 11:40:00 |
| Presentation Time | 50 minutes |
| Registration for | PRMU |
| Paper # | PRMU2007-139 |
| Volume (vol) | vol.107 |
| Number (no) | no.384 |
| Page | p.25 |
| #Pages | 1 |
| Date of Issue | 2007-12-06 (PRMU) |
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