Implicant Network: an associative memory model

摘要

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.