A robust method for distinguishing between learned and spurious attractors.
Neural Netw
; 17(3): 313-26, 2004 Apr.
Article
em En
| MEDLINE
| ID: mdl-15037350
ABSTRACT
Hopfield/constraint satisfaction type networks can be used to learn (autoassociate) patterns. Random inputs to the network will sometimes converge on states which are learned patterns, and sometimes converge on states which are unlearned/spurious. It would be useful for many reasons to be able to tell whether or not a given state was learned or spurious. In this paper we present a robust and general method, based on 'energy profiles', which allows us to make this distinction. We briefly describe related research, and note links with the study of recall, recognition and familiarity in the psychological literature.
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Base de dados:
MEDLINE
Assunto principal:
Aprendizagem por Associação
/
Inteligência Artificial
/
Redes Neurais de Computação
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Dinâmica não Linear
/
Modelos Neurológicos
Limite:
Humans
Idioma:
En
Ano de publicação:
2004
Tipo de documento:
Article