ON the influence of parameter theta- on performance of RBF neural networks trained with the dynamic decay adjustment algorithm

The dynamic decay adjustment (DDA) algorithm is a fast constructive algorithm for training RBF neural networks (RBFNs) and probabilistic neural networks (PNNs). The algorithm has two parameters, namely, theta(+) and theta(-). The papers which introduced DDA argued that those parameters would not hea...

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Veröffentlicht in:International journal of neural systems Jg. 16; H. 4; S. 271
Hauptverfasser: Oliveira, Adriano L I, Medeiros, Ericles A, Rocha, Thyago A B V, Bezerra, Miguel E R, Veras, Ronaldo C
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore 01.08.2006
Schlagworte:
Algorithms
Artificial Intelligence
Models, Statistical
Neural Networks (Computer)
Nonlinear Dynamics
Time Factors
ISSN:0129-0657
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Zusammenfassung:The dynamic decay adjustment (DDA) algorithm is a fast constructive algorithm for training RBF neural networks (RBFNs) and probabilistic neural networks (PNNs). The algorithm has two parameters, namely, theta(+) and theta(-). The papers which introduced DDA argued that those parameters would not heavily influence classification performance and therefore they recommended using always the default values of these parameters. In contrast, this paper shows that smaller values of parameter theta(-) can, for a considerable number of datasets, result in strong improvement in generalization performance. The experiments described here were carried out using twenty benchmark classification datasets from both Proben1 and the UCI repositories. The results show that for eleven of the datasets, the parameter theta(-) strongly influenced classification performance. The influence of theta(-) was also noticeable, although much less, on six of the datasets considered. This paper also compares the performance of RBF-DDA with theta(-) selection with both AdaBoost and Support Vector Machines (SVMs).
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ISSN:0129-0657
DOI:10.1142/S0129065706000676