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Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where `unknown' faults may occur

Li, Yuhua, Pont, Michael J. and Barrie Jones, N. 2002. Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where `unknown' faults may occur. Pattern recognition letters 23 (5) , pp. 569-577. 10.1016/S0167-8655(01)00133-7

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Abstract

This paper presents a novel technique which may be used to determine an appropriate threshold for interpreting the outputs of a trained radial basis function (RBF) classifier. Results from two experiments demonstrate that this method can be used to improve the performance of RBF classifiers in practical applications.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 0167-8655
Last Modified: 22 Feb 2019 15:35
URI: http://orca.cf.ac.uk/id/eprint/109849

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