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 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) |
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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) |
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Keyword(1) |
Function approximation |
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Neural network |
Keyword(3) |
Redundant dimension |
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Pattern coding |
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Parallel perceptron |
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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 |
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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 |
6 |
Date of Issue |
2017-06-16 (NC) |
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