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dc.contributor.authorDesmet, Pierre
dc.date.accessioned2010-02-25T12:29:01Z
dc.date.available2010-02-25T12:29:01Z
dc.date.issued2000-01
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/3581
dc.language.isoenen
dc.subjectNeural networksen
dc.subjectStatistical learning networksen
dc.subjectHedonic pricesen
dc.subjectConnexionismen
dc.subject.ddc658.8en
dc.subject.classificationjelZ13en
dc.subject.classificationjelL14en
dc.subject.classificationjelM11en
dc.subject.classificationjelM31en
dc.titleRelative performance of the statistical learning network: An application of the price-quality relationship in the automobileen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThe design and topology of a neural network is still an important and difficult task. To solve the problems of topology posed by the introduction of connexionism, new approaches are proposed, and especially a combination of induction rules with a statistical estimation of the neuron coefficients for each layer. This research aims to compare an algorithm of this SLN approach with traditional methods (regression and classical BP neural networks) using the gradient method. Methods are put into application to determine the price-quality relationship of a complex product, the automobile, according to the hedonic price model. This application of the price-quality relationship to the English automobile market leads to the conclusion that the claimed superiority of this approach is unsubstantiated since, compared to the BP neural networks and even linear regression, the performance of the GMDH method is inferior.en
dc.relation.isversionofjnlnameEuropean Journal of Economic and Social Systems
dc.relation.isversionofjnlvol14en
dc.relation.isversionofjnlissue1en
dc.relation.isversionofjnldate2000-01
dc.relation.isversionofjnlpages69-79en
dc.relation.isversionofdoi10.1051/ejess:2000109en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherEDP sciencesen
dc.subject.ddclabelMarketingen


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