Two approximations to learning from examples

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

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