Anthony J. B. Fogg

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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)
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)
This study uses a new data visualization method, developed by the first author, to investigate the reliability of a real world low-back-pain Multi-layer Perceptron (MLP) network from a hidden layer decision region perspective. Using decision region identification information from an explanation facility, the MLP training examples are discovered to occupy(More)
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