Recruitment learning of boolean functions in sparse random networks.
Int J Neural Syst
; 11(6): 537-59, 2001 Dec.
Article
em En
| MEDLINE
| ID: mdl-11852438
ABSTRACT
This work presents a new class of neural network models constrained by biological levels of sparsity and weight-precision, and employing only local weight updates. Concept learning is accomplished through the rapid recruitment of existing network knowledge - complex knowledge being realised as a combination of existing basis concepts. Prior network knowledge is here obtained through the random generation of feedforward networks, with the resulting concept library tailored through distributional bias to suit a particular target class. Learning is exclusively local - through supervised Hebbian and Winnow updates - avoiding the necessity for backpropagation of error and allowing remarkably rapid learning. The approach is demonstrated upon concepts of varying difficulty, culminating in the well-known Monks and LED benchmark problems.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Simulação por Computador
/
Redes Neurais de Computação
/
Aprendizagem
Tipo de estudo:
Clinical_trials
Idioma:
En
Revista:
Int J Neural Syst
Assunto da revista:
ENGENHARIA BIOMEDICA
/
INFORMATICA MEDICA
Ano de publicação:
2001
Tipo de documento:
Article
País de afiliação:
Austrália