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| Presentation | 2012-03-14 13:20 Training Multi-layered Neural Network Neocognitron Kunihiko Fukushima  NC2011-128 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper proposes new learning rules suited for training multi-layered neural networks and apply them to the neocognitron. The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. It acquires the ability to recognize visual patterns through learning. For training intermediate stages of the hierarchical network of the neocognitron, we use a new learning rule named add-if-silent. For the highest stage of the network, we use the method of \textit{interpolating vectors}, not only during recognition, but also during learning. By the use of add-if-silent rule, the learning algorithm is considerably simplified, and parameters required in designing the network can be reduced. The method of interpolating vectors greatly increases the recognition rate. Computer simulation demonstrates that the new neocognitron shows a higher recognition rate with a smaller scale of the network than the neocognitron of previous versions. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | visual pattern recognition / neocognitron / hierarchical neural network / learning rule / add-if-silent / interpolating vectors / / | |
| Reference Info. | IEICE Tech. Rep., vol. 111, no. 483, NC2011-128, pp. 39-44, March 2012. | |
| Paper # | NC2011-128 | |
| Date of Issue | 2012-03-07 (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 | NC2011-128 |
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| Conference Information | |
| Committee | MBE NC |
| Conference Date | 2012-03-14 - 2012-03-16 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tamagawa University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2012-03-MBE-NC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Training Multi-layered Neural Network Neocognitron |
| Sub Title (in English) | |
| Keyword(1) | visual pattern recognition |
| Keyword(2) | neocognitron |
| Keyword(3) | hierarchical neural network |
| Keyword(4) | learning rule |
| Keyword(5) | add-if-silent |
| Keyword(6) | interpolating vectors |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Kunihiko Fukushima |
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| Speaker | Author-1 |
| Date Time | 2012-03-14 13:20:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2011-128 |
| Volume (vol) | vol.111 |
| Number (no) | no.483 |
| Page | pp.39-44 |
| #Pages | 6 |
| Date of Issue | 2012-03-07 (NC) |
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