relation: https://khub.utp.edu.my/scholars/3095/
title: Semi batch learning with store management using enhanced conjugate gradient
creator: Asirvadam, V.S.
creator: Izzeldin, H.T.A.
creator: Saad, N.
creator: Mcloone, S.F.
description: This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.
date: 2012
type: Article
type: PeerReviewed
identifier:   Asirvadam, V.S. and Izzeldin, H.T.A. and Saad, N. and Mcloone, S.F.  (2012) Semi batch learning with store management using enhanced conjugate gradient.  Lecture Notes in Electrical Engineering, 136 LN.  pp. 61-67.  ISSN 18761100     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856034373&doi=10.1007%2f978-3-642-26001-8_9&partnerID=40&md5=5be7a75695b28fc4461cb92d0d0e7a84
relation: 10.1007/978-3-642-26001-8₉
identifier: 10.1007/978-3-642-26001-8₉