relation: https://khub.utp.edu.my/scholars/423/
title: Adaptive regularizer for recursive neural network training algorithms
creator: Asirvadam, V.S.
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.
date: 2008
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Asirvadam, V.S.  (2008) Adaptive regularizer for recursive neural network training algorithms.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-55849101672&doi=10.1109%2fCSEW.2008.55&partnerID=40&md5=d4333cf55459294e676abc6bc354cff7
relation: 10.1109/CSEW.2008.55
identifier: 10.1109/CSEW.2008.55