Steven J. Cavill

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Using a new method published by the first author, this chapter shows how knowledge in the form of a ranked data relationship and an induced rule can be directly extracted from each training case for a Multilayer Perceptron (MLP) network with binary inputs. The knowledge extracted from all training cases can be used to validate the MLP network and the ranked(More)
This paper interprets the outputs from a Multilayer Perceptron (MLP) network that performs a whole life assurance risk assessment task. Using a new method published by the first author, the paper finds the significant, or key, inputs that the network uses to classify applicants for whole life assurance into standard and non-standard risk. The ranking of the(More)
Using a new method published by the first author, this article shows how direct explanations can be provided to interpret the classification of any input case by a standard multilayer perceptron (MLP) network. The method is demonstrated for a real-world MLP that classifies low-back-pain patients into three diagnostic classes. The application of the method(More)
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