Multi-layer perceptron (MLP) is now widely used in classification problems, whereas radial basis function networks (RBFs) appear to be rather less well known. Purpose of this work is to briefly recall RBF networks, and to allow a synthesis of theirs best features. The application of these networks to the forensic glass data set (Ripley, 1996) tries to lay out what is common and what is distinctive in these networks and other well developed methodological tools for classification, and to compare numerical performances.
Using radial basis function networks for classification problems / Morlini, Isabella. - STAMPA. - (1999), pp. 73-76. (Intervento presentato al convegno II Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society tenutosi a Roma, Italy nel 3-4 Luglio 199).
Using radial basis function networks for classification problems
MORLINI, Isabella
1999
Abstract
Multi-layer perceptron (MLP) is now widely used in classification problems, whereas radial basis function networks (RBFs) appear to be rather less well known. Purpose of this work is to briefly recall RBF networks, and to allow a synthesis of theirs best features. The application of these networks to the forensic glass data set (Ripley, 1996) tries to lay out what is common and what is distinctive in these networks and other well developed methodological tools for classification, and to compare numerical performances.
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