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Paper Abstract and Keywords
Presentation 2012-03-14 16:00
Incremental Learning for regression on a budget
Koichiro Yamauchi (Chubu Univ) NC2011-145
Abstract (in Japanese) (See Japanese page) 
(in English) In our previous work, we had proposed a limited general regression neural network (LGRNN) for embedded systems.
The LGRNN learns new instances one by one for regression on a budget.
The LGRNN is an improved version of general regression neural network that continues incremental learning under a fixed number of hidden units.

Initially, the LGRNN learns new samples incrementally by allocating new hidden units.
If the number of hidden units reaches the upper bound, the LGRNN has to remove one useless hidden unit to learn a new sample.
However, there are cases in which the adverse effects of removing a useless unit are greater than the positive effects of learning the new sample.
In this case, the LGRNN should refrain from learning the new sample.
To achieve this, the LGRNN predicts the effects of several learning options (e.g., ignore or learning) before the learning process begins, and chooses the best learning option to be executed.

We also compared LGRNN's performances with those of other similar methods: Forgetron, Projectron and PDM.
As a result, we found that LGRNN error converges much faster than the other mothods.
Keyword (in Japanese) (See Japanese page) 
(in English) Limited General Regression Neural Networks / Kernel Machine / Approximated Linear Dependency / Incremental Learning / Learning on a budget / / /  
Reference Info. IEICE Tech. Rep., vol. 111, no. 483, NC2011-145, pp. 141-146, March 2012.
Paper # NC2011-145 
Date of Issue 2012-03-07 (NC) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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)
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Conference Information
Committee MBE NC  
Conference Date 2012-03-14 - 2012-03-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Tamagawa University 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NC 
Conference Code 2012-03-MBE-NC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Incremental Learning for regression on a budget 
Sub Title (in English)  
Keyword(1) Limited General Regression Neural Networks  
Keyword(2) Kernel Machine  
Keyword(3) Approximated Linear Dependency  
Keyword(4) Incremental Learning  
Keyword(5) Learning on a budget  
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1st Author's Name Koichiro Yamauchi  
1st Author's Affiliation Chyubu University (Chubu Univ)
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Speaker Author-1 
Date Time 2012-03-14 16:00:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2011-145 
Volume (vol) vol.111 
Number (no) no.483 
Page pp.141-146 
#Pages
Date of Issue 2012-03-07 (NC) 


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