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Paper Abstract and Keywords
Presentation 2011-11-09 16:20
Robust Training of the Feedforward Neural Networks using hybrid quasi-Newton Training Algorithm
Toshikazu Abe, Yoshihiko Sakashita, Hiroshi Ninomiya (SIT) NLP2011-105
Abstract (in Japanese) (See Japanese page) 
(in English) Various techniques based on the gradient descent method have been studied as training algorithms for neuralnetworks. Neural network training poses data-driven optimization problems in which the objective function involves thesummation of loss terms over a set of data to be modeled. For a given set of training data，the gradient-based algorithmsoperate in one of two modes: stochastic (online) or batch. Recently，the robust training algorithm based on quasi-Newtonmethod has been introduced improving the feeding-technique of training data. The algorithm combines the “stochastic(online)” mode with the “batch” one.The new learning method which online and batch error function are associable by a weighing coefficientparameter was introduced. In this paper，the neural training algorithm is which two technique are hybridized, is proposed. The proposed algorithm isverified about the validity of training for feedforward neural networksusing computer simulation .
Keyword (in Japanese) (See Japanese page) 
(in English) feedforword neural network / quasi-newton method / online training algorithm / batch training algorithm / / / /  
Reference Info. IEICE Tech. Rep., vol. 111, no. 276, NLP2011-105, pp. 75-80, Nov. 2011.
Paper # NLP2011-105 
Date of Issue 2011-11-02 (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)
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Conference Information
Committee NLP  
Conference Date 2011-11-09 - 2011-11-11 
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 2011-11-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Robust Training of the Feedforward Neural Networks using hybrid quasi-Newton Training Algorithm 
Sub Title (in English)  
Keyword(1) feedforword neural network  
Keyword(2) quasi-newton method  
Keyword(3) online training algorithm  
Keyword(4) batch training algorithm  
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Keyword(6)  
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1st Author's Name Toshikazu Abe  
1st Author's Affiliation Shonan Institute of Technology (SIT)
2nd Author's Name Yoshihiko Sakashita  
2nd Author's Affiliation Shonan Institute of Technology (SIT)
3rd Author's Name Hiroshi Ninomiya  
3rd Author's Affiliation Shonan Institute of Technology (SIT)
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Speaker Author-1 
Date Time 2011-11-09 16:20:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2011-105 
Volume (vol) vol.111 
Number (no) no.276 
Page pp.75-80 
#Pages 6 
Date of Issue 2011-11-02 (NLP) 

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