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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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Main Author: Asirvadam, Vijanth Sagayan
Format: Conference or Workshop Item
Published: 2008
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access: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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spelling 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/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Asirvadam, Vijanth Sagayan
Adaptive regularizer for recursive neural network training algorithms
description 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.
format Conference or Workshop Item
author Asirvadam, Vijanth Sagayan
author_facet Asirvadam, Vijanth Sagayan
author_sort Asirvadam, Vijanth Sagayan
title Adaptive regularizer for recursive neural network training algorithms
title_short Adaptive regularizer for recursive neural network training algorithms
title_full Adaptive regularizer for recursive neural network training algorithms
title_fullStr Adaptive regularizer for recursive neural network training algorithms
title_full_unstemmed Adaptive regularizer for recursive neural network training algorithms
title_sort adaptive regularizer for recursive neural network training algorithms
publishDate 2008
url 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/
_version_ 1738655046150651904
score 13.154949

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