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primary topic: Adaptive regularizer for recursive neural network training algorithms

Adaptive regularizer for recursive neural network training algorithms
type: AcademicArticle
type: Article
abstract: 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.
date: 2008
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

Proceedings of the 11th IEEE International Conference on Computational Science and Engineering, CSE Workshops 2008
type: Collection

V.S. Asirvadam
type: Person
given name: V.S.
family name: Asirvadam

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primary topic: Adaptive regularizer for recursive neural network training algorithms