Improving constructive training of RBF networks through selective pruning and model selection

This letter proposes a constructive training method for radial basis function networks. The proposed method is an extension of the dynamic decay adjustment (DDA) algorithm, a fast constructive algorithm for classification problems. The proposed method, which is based on selective pruning and DDA mod...

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 64; S. 537 - 541
Hauptverfasser: Oliveira, Adriano L.I., Melo, Bruno J.M., Meira, Silvio R.L.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.03.2005
Schlagworte:
Classification
Model complexity
Neural network
RBF network
RBF network
Neural network
Model complexity
Classification
ISSN:0925-2312, 1872-8286
Online-Zugang:Volltext
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Zusammenfassung:This letter proposes a constructive training method for radial basis function networks. The proposed method is an extension of the dynamic decay adjustment (DDA) algorithm, a fast constructive algorithm for classification problems. The proposed method, which is based on selective pruning and DDA model selection, aims to improve the generalization performance of DDA without generating larger networks. Simulations using four image recognition datasets from the UCI repository demonstrate the validity of the proposed method.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2004.11.027