Multi-Layer Perceptrons and Symbolic Data

Rossi, Fabrice; Conan-Guez, Brieuc

Rossi, Fabrice; Conan-Guez, Brieuc (2008), Multi-Layer Perceptrons and Symbolic Data, dans Diday, Edwin; Noirhomme-Fraiture, Monique, Symbolic Data Analysis and the SODAS Software, Wiley : Chichester (UK), p. 373-391

Type

Chapitre d'ouvrage

Lien vers un document non conservé dans cette base

http://hal.inria.fr/inria-00232878/en/

Date

2008

Titre de l'ouvrage

Symbolic Data Analysis and the SODAS Software

Auteurs de l’ouvrage

Diday, Edwin; Noirhomme-Fraiture, Monique

Éditeur

Wiley

Ville d’édition

Chichester (UK)

Isbn

978-0-470-01883-5

Nombre de pages

457

Pages

373-391

Métadonnées

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Auteur(s)

Rossi, Fabrice
Conan-Guez, Brieuc

Résumé (EN)

In 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.

Mots-clés

Symbolic Data; Interval Data; Multi-Layer Perceptron