Locally regularised two-stage learning algorithm for RBF network centre selection
Data(s) |
01/01/2011
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Direitos |
info:eu-repo/semantics/restrictedAccess |
Fonte |
Deng , J , Li , K & Irwin , G 2011 , ' Locally regularised two-stage learning algorithm for RBF network centre selection ' International Journal of Systems Science , vol 1 , pp. 1-14 . |
Tipo |
article |