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| Presentation | 2013-01-24 12:50 Study of qusai-Newton training algorithm on parallel distributed environment Makoto Saiki, Yoshihiko Sakashita, Hiroshi Ninomiya (Shonan Inst. of Tech.)  NLP2012-111 NC2012-101 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper describes the feasibility of quasi-Newton method for training feedforward neural networks on the parallel distributed environment. Recently, we have to deal with large data in machine learning. Parallel distributed environment is one of the means to solve this problem. Most of the learning method with huge samples are based on the first order gradient algorithms. The quasi-Newton method is one of the most effective method to training feed forward neural networks. This study verify practiced effectiveness method through computer simulations. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | neural networks / quasi-Newton method / back-propagation method / parallel distributed environment / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 112, no. 389, NLP2012-111, pp. 43-48, Jan. 2013. | |
| Paper # | NLP2012-111 | |
| Date of Issue | 2013-01-17 (NLP, NC) | |
| 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 | NLP2012-111 NC2012-101 |
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| Conference Information | |
| Committee | NC NLP |
| Conference Date | 2013-01-24 - 2013-01-25 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hokkaido University Centennial Memory Hall |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NLP |
| Conference Code | 2013-01-NC-NLP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Study of qusai-Newton training algorithm on parallel distributed environment |
| Sub Title (in English) | |
| Keyword(1) | neural networks |
| Keyword(2) | quasi-Newton method |
| Keyword(3) | back-propagation method |
| Keyword(4) | parallel distributed environment |
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| 1st Author's Name | Makoto Saiki |
| 1st Author's Affiliation | Shonan Institute of Technology (Shonan Inst. of Tech.) |
| 2nd Author's Name | Yoshihiko Sakashita |
| 2nd Author's Affiliation | Shonan Institute of Technology (Shonan Inst. of Tech.) |
| 3rd Author's Name | Hiroshi Ninomiya |
| 3rd Author's Affiliation | Shonan Institute of Technology (Shonan Inst. of Tech.) |
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| Speaker | Author-1 |
| Date Time | 2013-01-24 12:50:00 |
| Presentation Time | 20 minutes |
| Registration for | NLP |
| Paper # | NLP2012-111, NC2012-101 |
| Volume (vol) | vol.112 |
| Number (no) | no.389(NLP), no.390(NC) |
| Page | pp.43-48 |
| #Pages | 6 |
| Date of Issue | 2013-01-17 (NLP, NC) |
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