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 Multi-layer Perceptron (MLP) network with binary inputs. The knowledge extracted from all training cases can be used to validate the MLP network and the(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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