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Paper Abstract and Keywords
Presentation 2017-06-23 16:00
Analysis of Robustness of Approximators Based on Neural Networks Against Redundant Dimensions
Shoichi Someno, Tomohiro Tanno, Kazumasa Horie, Jun Izawa, Tomoki Ichiba, Masahiko Morita (Tsukuba Univ.) NC2017-8
Abstract (in Japanese) (See Japanese page) 
(in English) Redundant input dimensions that are not related to the output are known to lower the approximate accuracy of function approximators, but it is unclear which approximator is especially sensitive or robust to them. The present study compared the robustness of several neural network based approximators against redundant dimensions through numerical experiments on several simple fuinction approximation tasks. As a result, the approximator based on the method of pattern coding and the network of parallel perceptron was robust not only to redundant dimensions but also to partly-redundant dimensions that are not completely redundant but partly relevant to the output. Furthermore, the results implied that this approximator may be able to contribute to specify which input dimensions are redundant.
Keyword (in Japanese) (See Japanese page) 
(in English) Function approximation / Neural network / Redundant dimension / Pattern coding / Parallel perceptron / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 109, NC2017-8, pp. 21-26, June 2017.
Paper # NC2017-8 
Date of Issue 2017-06-16 (NC) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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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 NC2017-8

Conference Information
Committee NC IPSJ-BIO IBISML IPSJ-MPS  
Conference Date 2017-06-23 - 2017-06-25 
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 2017-06-NC-BIO-IBISML-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Analysis of Robustness of Approximators Based on Neural Networks Against Redundant Dimensions 
Sub Title (in English)  
Keyword(1) Function approximation  
Keyword(2) Neural network  
Keyword(3) Redundant dimension  
Keyword(4) Pattern coding  
Keyword(5) Parallel perceptron  
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Keyword(8)  
1st Author's Name Shoichi Someno  
1st Author's Affiliation Tsukuba University (Tsukuba Univ.)
2nd Author's Name Tomohiro Tanno  
2nd Author's Affiliation Tsukuba University (Tsukuba Univ.)
3rd Author's Name Kazumasa Horie  
3rd Author's Affiliation Tsukuba University (Tsukuba Univ.)
4th Author's Name Jun Izawa  
4th Author's Affiliation Tsukuba University (Tsukuba Univ.)
5th Author's Name Tomoki Ichiba  
5th Author's Affiliation Tsukuba University (Tsukuba Univ.)
6th Author's Name Masahiko Morita  
6th Author's Affiliation Tsukuba University (Tsukuba Univ.)
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Speaker Author-1 
Date Time 2017-06-23 16:00:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2017-8 
Volume (vol) vol.117 
Number (no) no.109 
Page pp.21-26 
#Pages
Date of Issue 2017-06-16 (NC) 


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