Extract intelligible and concise fuzzy rules from neural networks
Fuzzy Sets and Systems - Fuzzy systems
Determining Hyper-planes to Generate Symbolic Rules
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Knowledge based descriptive neural networks
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
A model for single and multiple knowledge based networks
Artificial Intelligence in Medicine
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We present a method to unify the rules obtained by the M-of-N rule-extraction technique. The rules extracted from a perceptron by the M-of-N algorithm are in correspondence with sets of minimal Boolean vectors with respect to the classical partial order defined on vectors. Our method relies on a simple characterization of another partial order defined on Boolean vectors. We show that there exists also a correspondence between sets of minimal Boolean vectors with respect to this order and M-of-N rules equivalent to a perceptron. The gain is that fewer rules are generated with the second order. Independently, we prove that deciding whether a perceptron is symmetric with respect to two variables is NP-complete