Presentation 2011-01-24
Parameterized online quasi-Newton Training Algorithm for Feedforward Neural Networks
Hiroshi NINOMIYA,
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Abstract(in English) This paper describes a new gradient based technique for training of feedforward neural networks. Recently, improved online quasi-Newton method was developed for neural network training improving feeding method of training data. This paper proposes a novel training algorithm based on the online quasi-Newton in which the feeding method of training data is parameterized. Furthermore, an analogy between the proposed algorithm and Langevin one is considered. The proposed algorithm is employed for robust neural network training purpose. Neural network training for some benchmark problems is presented to demonstrate the proposed algorithm. The proposed algorithm achieves more accurate and robust training results than the other quasi-Newton based training algorithms.
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Keyword(in English) feedforward neural networks / quasi-Newton method / online gradient training method / batch gradient training method / Langevin algorithm
Paper # NLP2010-125,NC2010-89
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Committee NC
Conference Date 2011/1/17(1days)
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Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Parameterized online quasi-Newton Training Algorithm for Feedforward Neural Networks
Sub Title (in English)
Keyword(1) feedforward neural networks
Keyword(2) quasi-Newton method
Keyword(3) online gradient training method
Keyword(4) batch gradient training method
Keyword(5) Langevin algorithm
1st Author's Name Hiroshi NINOMIYA
1st Author's Affiliation Department of Information Science, Faculty of Engineering, Shonan Institute of Technology()
Date 2011-01-24
Paper # NLP2010-125,NC2010-89
Volume (vol) vol.110
Number (no) 388
Page pp.pp.-
#Pages 6
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