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Algorithms 2009, 2(3), 1045-1068; doi:10.3390/a2031045

Radial Basis Function Cascade Correlation Networks

Department of Chemistry and Biochemistry, Ohio University, Athens, OH 45701, USA
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Received: 1 July 2009 / Revised: 31 July 2009 / Accepted: 21 August 2009 / Published: 27 August 2009
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
View Full-Text   |   Download PDF [386 KB, uploaded 27 August 2009]   |  

Abstract

A cascade correlation learning architecture has been devised for the first time for radial basis function processing units. The proposed algorithm was evaluated with two synthetic data sets and two chemical data sets by comparison with six other standard classifiers. The ability to detect a novel class and an imbalanced class were demonstrated with synthetic data. In the chemical data sets, the growth regions of Italian olive oils were identified by their fatty acid profiles; mass spectra of polychlorobiphenyl compounds were classified by chlorine number. The prediction results by bootstrap Latin partition indicate that the proposed neural network is useful for pattern recognition. View Full-Text
Keywords: cascade correlation; radial basis function; artificial neural networks; bootstrap Latin partition cascade correlation; radial basis function; artificial neural networks; bootstrap Latin partition
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Lu, W.; Harrington, P.B. Radial Basis Function Cascade Correlation Networks. Algorithms 2009, 2, 1045-1068.

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