Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21890
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dc.contributor.advisorBOUTEN, Marcus-
dc.contributor.authorSchietse, Jan-
dc.date.accessioned2016-08-04T09:05:34Z-
dc.date.available2016-08-04T09:05:34Z-
dc.date.issued1996-
dc.identifier.urihttp://hdl.handle.net/1942/21890-
dc.description.abstractNot available-
dc.language.isoen-
dc.subject.otherArtificial neural networks, repulsive potential, Bayesian learning, Learning rules, Gradient descent learning, Gaussian model, Gibbs learning, Ising teacher problem-
dc.titleTowards Bayesian learning for the perceptron-
dc.typeTheses and Dissertations-
local.format.pages147-
local.bibliographicCitation.jcatT1-
local.type.specifiedPhd thesis-
item.accessRightsOpen Access-
item.contributorSchietse, Jan-
item.fullcitationSchietse, Jan (1996) Towards Bayesian learning for the perceptron.-
item.fulltextWith Fulltext-
Appears in Collections:PhD theses
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