Communications of the ACM
A factor spaces approach to knowledge representation
Fuzzy Sets and Systems - Fuzzy information processing
Skeletonization: a technique for trimming the fat from a network via relevance assessment
Advances in neural information processing systems 1
Comparing biases for minimal network construction with back-propagation
Advances in neural information processing systems 1
A Nearest Hyperrectangle Learning Method
Machine Learning
Advances in neural information processing systems 2
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
The role of Hypothesis In medical diagnosis
IJCAI'75 Proceedings of the 4th international joint conference on Artificial intelligence - Volume 1
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In this paper, we propose a framework for integrating fault diagnosis and incremental knowledge acquisition in connectionist expert systems. A new case solved by the Diagnostic Function is formulated as a new example for the Learning Function to learn incrementally. The Diagnostic Function is composed of a neural networks-based Example Module and a symbolic-based Rule Module. While the Example Module is always first invoked to provide the shortcut solution, the Rule Module provides extensive coverage of cases to handle odd cases when Example Module fails. Two applications based on the proposed framework will also be briefly mentioned.