Jonathan Francis Dale Addison

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This paper examines the performance of seven neural network architectures in classifying and detecting novel events contained within data collected from turbine sensors. Several different multi-layer perceptrons were built and trained using back propagation, conjugate gradient and Quasi-Newton training algorithms. In addition, Linear networks, Radial Basis(More)
We have applied several dimensionality reduction techniques to data modelling using neural network architectures for classification using a number of data sets. The reduction methods considered include both linear and non linear forms of principal components analysis, genetic algorithms and sensitivity analysis. The results of each were used as inputs to(More)
A criticism of neural network architectures is their susceptibility to “catastrophic interference” the ability to forget previously learned data when presented with new patterns. To avoid this, neural network architectures have been developed which specifically provide the network with a memory, either through the use of a context unit, which can store(More)
This work considers the applicability of applying the derivatives of stepwise linear regression modelling (specifically the p-values which indicate the importance of a variable to the modelling process) as a feature extraction technique. We utilise it in conjunction with several data sets of varying levels of complexity, and compare our results to other(More)
Many companies are now developing an online internet presence to sell or promote their products and services. The data generated by e-commerce sites is a valuable source of business knowledge but only if it is correctly analyzed. Data mining web server logs is now an important application area for business strategy. We describe an e-commerce system(More)
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