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Multi-Layer Perceptrons and Symbolic Data

dc.contributor.authorRossi, Fabrice
HAL ID: 77		en_US
dc.contributor.authorConan-Guez, Brieuc
HAL ID: 21890		en_US
dc.date.accessioned2009-06-23T13:34:31Z
dc.date.available2009-06-23T13:34:31Z
dc.date.issued2008
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/417
dc.language.isoenen_US
dc.subjectSymbolic Dataen_US
dc.subjectInterval Dataen_US
dc.subjectMulti-Layer Perceptronen_US
dc.subject.ddc519
dc.titleMulti-Layer Perceptrons and Symbolic Dataen_US
dc.typeChapitre d'ouvrageen_US
dc.description.abstractenIn some real world situations, linear models are not sufficient to represent accurately complex relations between input variables and output variables of a studied system. Multilayer Perceptrons are one of the most successful non-linear regression tool but they are unfortunately restricted to inputs and outputs that belong to a normed vector space. In this chapter, we propose a general recoding method that allows to use symbolic data both as inputs and outputs to Multilayer Perceptrons. The recoding is quite simple to implement and yet provides a flexible framework that allows to deal with almost all practical cases. The proposed method is illustrated on a real world data set.en_US
dc.identifier.citationpages373-391
dc.relation.ispartoftitleSymbolic Data Analysis and the SODAS Softwareen_US
dc.relation.ispartofeditorDiday, Edwinen_US
dc.relation.ispartofeditorNoirhomme-Fraiture, Monique
dc.relation.ispartofpublnameWileyen_US
dc.relation.ispartofpublcityChichester (UK)
dc.relation.ispartofdate2008en_US
dc.relation.ispartofpages457
dc.identifier.urlsitehttp://hal.inria.fr/inria-00232878/en/en_US
dc.subject.ddclabelProbabilités et mathématiques appliquées
dc.relation.ispartofisbn978-0-470-01883-5


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