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dc.contributor.authorRossi, Fabrice*
dc.contributor.authorDelannay, Nicolas*
dc.contributor.authorConan-Guez, Brieuc*
dc.contributor.authorVerleysen, Michel*
dc.date.accessioned2009-06-30T15:59:21Z
dc.date.available2009-06-30T15:59:21Z
dc.date.issued2005
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/589
dc.language.isoenen
dc.subjectFunctional data analysis; smooth data; projection on smooth bases; irregular sampling; missing dataen
dc.subject.ddc519en
dc.titleRepresentation of Functional Data in Neural Networksen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherINRIA;France
dc.contributor.editoruniversityotherUniversité Catholique de Louvain;Belgique
dc.description.abstractenFunctional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in the analysis methods. This paper shows how to extend the Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models to functional data inputs, in particular when the latter are known through lists of input-output pairs. Various possibilities for functional processing are discussed, including the projection on smooth bases, Functional Principal Component Analysis, functional centering and reduction, and the use of differential operators. It is shown how to incorporate these functional processing into the RBFN and MLP models. The functional approach is illustrated on a benchmark of spectrometric data analysis.en
dc.relation.isversionofjnlnameNeurocomputing
dc.relation.isversionofjnlvol64en
dc.relation.isversionofjnldate2005
dc.relation.isversionofjnlpages183--210en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.neucom.2004.11.012en
dc.identifier.urlsitehttp://hal.inria.fr/inria-00000666/en/en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherElsevier
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
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