DocumentCode
352908
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
Volume
4
fYear
2000
fDate
2000
Firstpage
23
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.860740
Filename
860740
Link To Document