A Neural Associative Pattern Classifier
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
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Neural Computation
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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The problem of spurious patterns in neural associative memory models is discussed. Some suggestions to solve this problem from the literature are reviewed and their inadequacies are pointed out. A solution based on the notion of neural self-interaction with a suitably chosen magnitude is presented for the Hebbian learning rule. For an optimal learning rule based on linear programming, asymmetric dilution of synaptic connections is presented as another solution to the problem of spurious patterns. With varying percentages of asymmetric dilution it is demonstrated numerically that this optimal learning rule leads to near total suppression of spurious patterns. For practical usage of neural associative memory networks a combination of the two solutions with the optimal learning rule is recommended to be the best proposition