Adaptive regularizer for recursive neural network training algorithms
Adaptive Marquardt parameter correction techniques are tested for recursive Levenberg-Marquardt (RLM) and proposed novel application on decomposed recursive Levenberg Marquardt (DRLM) algorithms. The adaptive Marquardt correction is based on recursive moving-window residual. Experiment results show...
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my.utp.eprints.2592017-01-19T08:26:24Z Adaptive regularizer for recursive neural network training algorithms Asirvadam, Vijanth Sagayan TK Electrical engineering. Electronics Nuclear engineering Adaptive Marquardt parameter correction techniques are tested for recursive Levenberg-Marquardt (RLM) and proposed novel application on decomposed recursive Levenberg Marquardt (DRLM) algorithms. The adaptive Marquardt correction is based on recursive moving-window residual. Experiment results show superior convergence using decomposed approach and a slight improvement in performance by adopting the adaptive Marquardt correction on a fixed size multilayer perceptions (MLP) network. © 2008 IEEE. 2008 Conference or Workshop Item NonPeerReviewed application/pdf http://eprints.utp.edu.my/259/1/paper.pdf http://www.scopus.com/inward/record.url?eid=2-s2.0-55849101672&partnerID=40&md5=269731419c26dfaff29ed744ee54d2b9 Asirvadam, Vijanth Sagayan (2008) Adaptive regularizer for recursive neural network training algorithms. In: 11th IEEE International Conference on Computational Science and Engineering, CSE Workshops 2008, 16 July 2008 through 18 July 2008, Sao Paulo, SP. http://eprints.utp.edu.my/259/ |
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TK Electrical engineering. Electronics Nuclear engineering Asirvadam, Vijanth Sagayan Adaptive regularizer for recursive neural network training algorithms |
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Adaptive Marquardt parameter correction techniques are tested for recursive Levenberg-Marquardt (RLM) and proposed novel application on decomposed recursive Levenberg Marquardt (DRLM) algorithms. The adaptive Marquardt correction is based on recursive moving-window residual. Experiment results show superior convergence using decomposed approach and a slight improvement in performance by adopting the adaptive Marquardt correction on a fixed size multilayer perceptions (MLP) network. © 2008 IEEE.
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Conference or Workshop Item |
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Asirvadam, Vijanth Sagayan |
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Asirvadam, Vijanth Sagayan |
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Asirvadam, Vijanth Sagayan |
title |
Adaptive regularizer for recursive neural network training algorithms
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title_short |
Adaptive regularizer for recursive neural network training algorithms
|
title_full |
Adaptive regularizer for recursive neural network training algorithms
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title_fullStr |
Adaptive regularizer for recursive neural network training algorithms
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Adaptive regularizer for recursive neural network training algorithms
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title_sort |
adaptive regularizer for recursive neural network training algorithms |
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2008 |
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http://eprints.utp.edu.my/259/1/paper.pdf http://www.scopus.com/inward/record.url?eid=2-s2.0-55849101672&partnerID=40&md5=269731419c26dfaff29ed744ee54d2b9 http://eprints.utp.edu.my/259/ |
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13.2014675 |