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Paper Abstract and Keywords
Presentation 2015-06-24 16:00
[Invited Talk] Deep Convolutional Neural Network Neocognitron and its Advances
Kunihiko Fukushima (FLSI) NC2015-3 IBISML2015-20
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
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

Conference Information
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  
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) 
Date of Issue 2015-06-16 (IBISML), 2015-06-17 (NC) 

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