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| Presentation | 2015-06-24 16:00 [Invited Talk] Deep Convolutional Neural Network Neocognitron and its Advances Kunihiko Fukushima (FLSI)  NC2015-3 IBISML2015-20 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The neocognitron is a multi-layered convolutional network that can be trained to recognize visual patterns robustly. In lower layers of the network, local visual features are extracted from input patterns. Extraction and integration of visual features are repeated in the intermediate layers, and higher-order features are gradually extracted. In the highest layer, input patterns are classified based on the features extracted by the intermediate layers. Although the neocognitron has a long history, modifications of the network to improve its performance are still going on. The neocognitron can be classified to a so-called deep convolutional neural network, but there are several differences in detail. Focusing on these differences, this paper discusses neocognitron of a recent version. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | visual pattern recognition / convolutional neural network / deep network / neocognitron / learning rule / add-if-silent / interpolating-vector / | |
| Reference Info. | IEICE Tech. Rep., vol. 115, no. 111, NC2015-3, pp. 49-54, June 2015. | |
| Paper # | NC2015-3 | |
| Date of Issue | 2015-06-16 (IBISML), 2015-06-17 (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 | NC2015-3 IBISML2015-20 |
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| Conference Information | |
| Committee | NC IPSJ-BIO IBISML IPSJ-MPS |
| Conference Date | 2015-06-23 - 2015-06-25 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa Institute of Science and Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Machine Learning Approach to Biodata Mining, and General |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2015-06-NC-BIO-IBISML-MPS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Deep Convolutional Neural Network Neocognitron and its Advances |
| Sub Title (in English) | |
| Keyword(1) | visual pattern recognition |
| Keyword(2) | convolutional neural network |
| Keyword(3) | deep network |
| Keyword(4) | neocognitron |
| Keyword(5) | learning rule |
| Keyword(6) | add-if-silent |
| Keyword(7) | interpolating-vector |
| Keyword(8) | |
| 1st Author's Name | Kunihiko Fukushima |
| 1st Author's Affiliation | Fuzzy Logic System Institute (FLSI) |
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| Speaker | Author-1 |
| Date Time | 2015-06-24 16:00:00 |
| Presentation Time | 50 minutes |
| Registration for | NC |
| Paper # | NC2015-3, IBISML2015-20 |
| Volume (vol) | vol.115 |
| Number (no) | no.111(NC), no.112(IBISML) |
| Page | pp.49-54(NC), pp.165-170(IBISML) |
| #Pages | 6 |
| Date of Issue | 2015-06-16 (IBISML), 2015-06-17 (NC) |
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