Evaluation of neural network performance and generalisation using thresholding functions

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Pierce, S.G. and Worden, K. and Manson, G. (2007) Evaluation of neural network performance and generalisation using thresholding functions. Neural Computing and Applications, 16 (2). pp. 109-124. ISSN 0941-0643 (https://doi.org/10.1007/s00521-006-0044-z)

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Abstract

The application of a simple thresholding technique to help assess the satisfactory performance of classification networks formed from Multi-Layer Perceptron (MLP) artificial neural networks (ANNs) is discussed. Both conventional Maximum Likelihood and Bayesian Evidence based training paradigms were implemented. Firstly a simulated data set drawn from a two-dimensional Gaussian distribution was investigated to illustrate the physical significance of the threshold plots compared to the classifier output probability contours. Secondly a real world application data set comprising of low-frequency vibration measurements on an aircraft wing (a GNAT trainer) is considered. It is demonstrated that simple threshold based plots applied to classifier network outputs may provide a simple yet powerful technique to aid in the rejection of poorly regularised network structures.

ORCID iDs

Pierce, S.G. ORCID logoORCID: https://orcid.org/0000-0003-0312-8766, Worden, K. and Manson, G.;
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