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On Learning Mean Values in Hopfield Associative Memories Trained with Noisy Examples Using the Hebb Rule
Como, Italy July 24-July 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.860740IEEE-INNS-ENNS International Joint Co ...
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Bruno Cernuschi-Frías, Universidad de Buenos Aires and CONICET.
Enrique C. Segura, Universidad de Buenos Aires and CONICET.
We study, using standard Probability Theory results, the ability of the Hopfield model of associative memory using the Hebb rule to learn mean values from examples in the presence of noise. We state and prove properties concerning this ability.
Index Terms:
Neural Networks, Unsupervised Learning, Classification, Clustering
Citation:
Bruno Cernuschi-Frías, Enrique C. Segura, "On Learning Mean Values in Hopfield Associative Memories Trained with Noisy Examples Using the Hebb Rule," ijcnn, vol. 4, pp.4023, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4, 2000
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