DocumentCode
2971155
Title
Concept learning in Hopfield associative memories trained with noisy examples using the Hebb rule
Author
Cernuschi-Frias, Bruno ; Segura, Enrique C.
Author_Institution
Buenos Aires Univ., Argentina
Volume
3
fYear
1993
fDate
25-29 Oct. 1993
Firstpage
2615
Abstract
The notion of concept learning is introduced. Here we consider a concept as the mean of some statistical distribution, from which the examples of this concept are drawn. We study, using standard probability theory results, the ability of the Hopfield model of associative memory using the Hebb rule to learn concepts from examples in the presence of noise. We state and prove properties concerning this ability.
Keywords
Hebbian learning; Hopfield neural nets; associative processing; content-addressable storage; probability; statistical analysis; Hebb rule; Hopfield associative memories; concept learning; probability theory; statistical distribution; Associative memory; Computer networks; Data mining; Hebbian theory; Hopfield neural networks; Neurons; Nonlinear dynamical systems; Probability; Statistical distributions; Zinc;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN
0-7803-1421-2
Type
conf
DOI
10.1109/IJCNN.1993.714260
Filename
714260
Link To Document