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
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;
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
DOI :
10.1109/IJCNN.1993.714260
