A neural network diagnosis model without disorder independence assumption

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Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1998, 1531 pp. 341 - 352
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© Springer-Verlag Berlin Heidelberg 1998. Generally, the disorders in a neural network diagnosis model are assumed independent each other. In this paper, we propose a neural network model for diagnostic problem solving where the disorder independence assumption is no longer necessary. Firstly, we characterize the diagnostic tasks and the causal network which is used to represent the diagnostic problem, then we describe the neural network diagnosis model, finally, some experiment results will be given.
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