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Paper Abstract and Keywords
Presentation 2013-07-09 10:00
Dynamic Sample Size Selection based quasi-Newton Training for Multilayer Neural Networks
Hiroshi Ninomiya (SIT) NLP2013-38
Abstract (in Japanese) (See Japanese page) 
(in English) This paper describes a novel robust training algorithm based on quasi-Newton iteration with the dynamic sample size selection The size of training samples for each iteration is dynamically and analytically determined by variance estimates during the computation of its gradient in the mini-batch training. Furthermore, the size of mini-batch is controlled by a parameter to ensure that the number of samples in a mini-batch changes from a portion of samples (online) to all ones (batch) as quasi-Newton iteration progressed. As a result, the proposed algorithm has strong ability to search a global minimum without being trapped into local minimum. Moreover the iteration during online mode can be shortened compared with previous quasi-Newton-based methods in which the gradient of error function for the training step was improved.
Keyword (in Japanese) (See Japanese page) 
(in English) multilayer neural networks / quasi-Newton method / online and batch training / sample size selection / / / /  
Reference Info. IEICE Tech. Rep., vol. 113, no. 116, NLP2013-38, pp. 63-68, July 2013.
Paper # NLP2013-38 
Date of Issue 2013-07-01 (NLP) 
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)
Download PDF NLP2013-38

Conference Information
Committee NLP  
Conference Date 2013-07-08 - 2013-07-09 
Place (in Japanese) (See Japanese page) 
Place (in English) Miyako Island Marine Terminal 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NLP 
Conference Code 2013-07-NLP 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Dynamic Sample Size Selection based quasi-Newton Training for Multilayer Neural Networks 
Sub Title (in English)  
Keyword(1) multilayer neural networks  
Keyword(2) quasi-Newton method  
Keyword(3) online and batch training  
Keyword(4) sample size selection  
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1st Author's Name Hiroshi Ninomiya  
1st Author's Affiliation Shonan Institute of Technology (SIT)
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Speaker Author-1 
Date Time 2013-07-09 10:00:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2013-38 
Volume (vol) vol.113 
Number (no) no.116 
Page pp.63-68 
#Pages 6 
Date of Issue 2013-07-01 (NLP) 

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