Two approximations to learning from examples



Document title: Two approximations to learning from examples
Journal: Brazilian journal of physics
Database: PERIÓDICA
System number: 000146995
ISSN: 0103-9733
Authors: 1
Institutions: 1Universidade de Sao Paulo, Instituto de Física, Sao Paulo. Brasil
Year:
Season: Dic
Volumen: 28
Number: 4
Pages: 453-461
Country: Brasil
Language: Inglés
Document type: Artículo
Approach: Analítico
English abstract We investigate the learning of a rule from examples of the case of boolean perceptron. Previous studies of this problem have been made using the full quenched theory. We consider here two alternative approaches that can be applied easily. the two-replicas interactions approach considerably improves upon the well-known first-order approach. The mean field approach proved some results that have been obtained previously using the complex full quenched theory. Both approximations have been applied to both continuous weights and discrete weights perceptron
Disciplines: Física y astronomía
Keyword: Física,
Termodinámica y física estadística,
Sistemas complejos,
Redes neuronales,
Aprendizaje,
Perceptrón booleano
Keyword: Physics and astronomy,
Physics,
Thermodynamics and statistical physics,
Complex systems,
Neural networks,
Learning,
Boolean perceptron
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