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| Presentation | 2017-03-07 11:30 A stochastic optimization method and generalization bounds for voting classifiers by continuous density functions Atsushi Nitanda (Tokyo Tech./NTTDATA MSI), Taiji Suzuki (Tokyo Tech./JST/RIKEN)  IBISML2016-108 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We consider a learning method for the majority vote classifier by probability measure on continuously parametrized space of base classifiers. We give generalization bounds on such classifiers by extending well known results for the convex combination. In order to solve the empirical risk minimization problem for this model, we propose a stochastic optimization method performs in a probability space and we give its convergence analysis. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | majority vote classifier / nonlinear classification / margin theory / stochastic particle gradient descent / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 500, IBISML2016-108, pp. 63-69, March 2017. | |
| Paper # | IBISML2016-108 | |
| Date of Issue | 2017-02-27 (IBISML) | |
| 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 | IBISML2016-108 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2017-03-06 - 2017-03-07 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokyo Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Statistical Mathematics, Machine Learning, Data Mining, etc. |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2017-03-IBISML |
| Language | English (Japanese title is available) |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A stochastic optimization method and generalization bounds for voting classifiers by continuous density functions |
| Sub Title (in English) | |
| Keyword(1) | majority vote classifier |
| Keyword(2) | nonlinear classification |
| Keyword(3) | margin theory |
| Keyword(4) | stochastic particle gradient descent |
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| 1st Author's Name | Atsushi Nitanda |
| 1st Author's Affiliation | Tokyo Institute of Technology/NTTDATA Mathematical Systems Inc. (Tokyo Tech./NTTDATA MSI) |
| 2nd Author's Name | Taiji Suzuki |
| 2nd Author's Affiliation | Tokyo Institute of Technology/Japan Science and Technology Agency/RIKEN (Tokyo Tech./JST/RIKEN) |
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| Speaker | Author-1 |
| Date Time | 2017-03-07 11:30:00 |
| Presentation Time | 30 minutes |
| Registration for | IBISML |
| Paper # | IBISML2016-108 |
| Volume (vol) | vol.116 |
| Number (no) | no.500 |
| Page | pp.63-69 |
| #Pages | 7 |
| Date of Issue | 2017-02-27 (IBISML) |
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