Two approximations to learning from examples

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DC Field	Value	Language
dc.creator	Diambra L.	-
dc.date	1998	-
dc.date.accessioned	2013-05-29T22:42:16Z	-
dc.date.available	2013-05-29T22:42:16Z	-
dc.date.issued	2013-05-30	-
dc.identifier	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-97331998000400020	-
dc.identifier	http://www.doaj.org/doaj?func=openurl&genre=article&issn=01039733&date=1998&volume=28&issue=4&spage=00	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/jspui/handle/123456789/2480	-
dc.description	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.	-
dc.publisher	Sociedade Brasileira de Física	-
dc.source	Brazilian Journal of Physics	-
dc.title	Two approximations to learning from examples	-
Appears in Collections:	Physics and Astronomy

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