Locally regularised two-stage learning algorithm for RBF network centre selection

Jing Deng, Kang Li, George Irwin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Nonlinear models constructed from radial basis function (RBF) networks can easily be over-fitted due to the noise on the data. While information criteria, such as the final prediction error (FPE), can provide a trade-off between training error and network complexity, the tunable parameters that penalise a large size of network model are hard to determine and are usually network dependent. This article introduces a new locally regularised, two-stage stepwise construction algorithm for RBF networks. The main objective is to produce a parsomous network that generalises well over unseen data. This is achieved by utilising Bayesian learning within a two-stage stepwise construction procedure to penalise centres that are mainly interpreted by the noise.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalInternational Journal of Systems Science
Volume1
Publication statusPublished - Jan 2011

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