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Two approximations to learning from examples

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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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