J. Phys. France 51, 2421-2430 (1990)
DOI: 10.1051/jphys:0199000510210242100

Generalization in a Hopfield network

J.F. Fontanari

Instituto de Fisica e Quimica de São Carlos, Universidade de São Paulo, Caixa Postal 369, 13560 São Carlos SP, Brasil

Abstract
The performance of a Hopfield network in learning an extensive number of concepts having access only to a finite supply of typical data which exemplify the concepts is studied. The minimal number of examples which must be taught to the network in order it starts to create representations for the concepts is calculated analitically. It is shown that the mixture states play a crucial role in the creation of these representations.

PACS
8718S - Neural networks.
8710 - General theory and mathematical aspects.
0705M - Neural networks, fuzzy logic, artificial intelligence.

Key words
learning systems -- neural nets -- neurophysiology