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Radial Basis Function Cascade Correlation Networks
Department of Chemistry and Biochemistry, Ohio University, Athens, OH 45701, USA
* Author to whom correspondence should be addressed.
Received: 1 July 2009; in revised form: 31 July 2009 / Accepted: 21 August 2009 / Published: 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.
Keywords: cascade correlation; radial basis function; artificial neural networks; bootstrap Latin partition
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MDPI and ACS Style
Lu, W.; Harrington, P.B. Radial Basis Function Cascade Correlation Networks. Algorithms 2009, 2, 1045-1068.
Lu W, Harrington PB. Radial Basis Function Cascade Correlation Networks. Algorithms. 2009; 2(3):1045-1068.
Lu, Weiying; Harrington, Peter de B. 2009. "Radial Basis Function Cascade Correlation Networks." Algorithms 2, no. 3: 1045-1068.