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Implicant Network: an associative memory model

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1 Author(s)
Federici, D. ; Dept. of Comput. & Inf. Sci., Norwegian Univ. of Sci. & Technol., Trondheim, Norway

The Implicant Network is a neural network model capable of storing an arbitrary boolean function F : {0, 1}n → {0, 1}. The difference from previous one-shot learning models is that the training algorithm compresses the positive set online with linear time and space requirements. The algorithm works by building a Sum Of Products (SOP) representation of the positive set as it is presented to the network. Since the minimum coverage of implicants is an NP-hard problem, the compression rate is not optimal at first but it is shown to increase rapidly as the positive set is shown over again.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:4 )

Date of Conference:

20-24 July 2003

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