Title :
On learning mean values in Hopfield associative memories trained with noisy examples using the Hebb rule
Author :
Cermuschi-Frais, B. ; Segura, Enrique C.
Author_Institution :
Fac. de Ingenieria, Buenos Aires Univ., Argentina
Abstract :
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
Keywords :
Hebbian learning; Hopfield neural nets; content-addressable storage; unsupervised learning; Hebb rule; Hopfield associative memories; Probability Theory; learning mean values; Associative memory; Computer networks; Data mining; Hebbian theory; Hopfield neural networks; Neural networks; Neurons; Nonlinear dynamical systems; Statistical distributions; Unsupervised learning;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.860740
