Paper Abstract and Keywords |
Presentation |
2017-06-23 16:25
Analysis of Sequential Learning Capability of Selective Desensitization Neural Network Tomoki Ichiba, Tomohiro Tanno, Kazumasa Horie, Jun Izawa, Syoichi Someno, Masahiko Morita (Tsukuba Univ.) NC2017-9 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
A conventional neural network with high generalization ability is known to entirely forget the previously learned information by sequential learning.
Recent applied researches have showed that selective desensitization neural network (SDNN) does not have this problem, but its factors are unclear.
The present study clarifies the capability and its factors of SDNN on sequential learning by analyzing the difference in characteristics of SDNN and conventional neural networks through numerical experiments of sequential learning on two-dimensional function approximation tasks.
As a result, SDNN was able to not only locally fit to each of the new data but also affect widely to complement between samples when several similar data gathered near.
These characteristics of SDNN contribute to its high capability to both preserve the previous model and effectively fit to the new data while keeping high generalization ability. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Function approximation / Neural network / Selective Desensitization Neural Network / Sequential learning / Global generalization / / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 109, NC2017-9, pp. 27-32, June 2017. |
Paper # |
NC2017-9 |
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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NC2017-9 |
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 Sequential Learning Capability of Selective Desensitization Neural Network |
Sub Title (in English) |
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Keyword(1) |
Function approximation |
Keyword(2) |
Neural network |
Keyword(3) |
Selective Desensitization Neural Network |
Keyword(4) |
Sequential learning |
Keyword(5) |
Global generalization |
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1st Author's Name |
Tomoki Ichiba |
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 |
Syoichi Someno |
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 16:25:00 |
Presentation Time |
25 minutes |
Registration for |
NC |
Paper # |
NC2017-9 |
Volume (vol) |
vol.117 |
Number (no) |
no.109 |
Page |
pp.27-32 |
#Pages |
6 |
Date of Issue |
2017-06-16 (NC) |