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hal.structure.identifierDauphine Recherches en Management [DRM]
dc.contributor.authorDesmet, Pierre
dc.date.accessioned2019-08-30T14:08:02Z
dc.date.available2019-08-30T14:08:02Z
dc.date.issued1998
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19617
dc.language.isoenen
dc.subjectempirical resultsen
dc.subjectdirect marketingen
dc.subjectscoringen
dc.subjectalgorithmen
dc.subjectbio mimeticen
dc.subjectneural networksen
dc.subject.ddc658.8en
dc.subject.classificationjelG.G2.G24en
dc.subject.classificationjelM.M3.M31en
dc.subject.classificationjelC.C4.C45en
dc.titleComparison of the Predictability of a Neural Network with Retropropagation with Those using Linear Regression, Logistic and A.I.D. Methods for Direct Marketing Scoringen
dc.typeChapitre d'ouvrage
dc.description.abstractenIn comparison with other usual statistical methods (MCO, logistic regression, discriminant analysis, AID), advantages of neural network with backpropagation are numerous and well known (non linear effects, distribution flee variables, low sensibility to outliers or missing variables). However, implementation and efficiency have not yet received a strong interest. The paper reviews comparative analyses and presents results obtained for prediction of a behaviour in Fund raising.en
dc.identifier.citationpages61-75en
dc.relation.ispartoftitleBio-Mimetic Approaches in Management Scienceen
dc.relation.ispartofeditorAurifeille, Jacques-Marie
dc.relation.ispartofeditorDeissenberg, Christophe
dc.relation.ispartofpublnameSpringeren
dc.relation.ispartofpublcityBerlin Heidelbergen
dc.relation.ispartofdate1998
dc.subject.ddclabelMarketing directen
dc.relation.ispartofisbn978-0792349938en
dc.relation.forthcomingnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2019-08-28T08:59:59Z
hal.author.functionaut


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