Multi-Layer Perceptrons and Symbolic Data

Abstract : 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.
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Chapitre d'ouvrage
Diday, Edwin and Noirhomme-Fraiture, Monique. Symbolic Data Analysis and the SODAS Software, Wiley, pp.373-391, 2008
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Contributeur : Fabrice Rossi <>
Soumis le : samedi 2 février 2008 - 15:50:55
Dernière modification le : mercredi 28 septembre 2016 - 16:01:12
Document(s) archivé(s) le : lundi 3 mai 2010 - 16:11:00

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  • HAL Id : inria-00232878, version 1
  • ARXIV : 0802.0251

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Fabrice Rossi, Brieuc Conan-Guez. Multi-Layer Perceptrons and Symbolic Data. Diday, Edwin and Noirhomme-Fraiture, Monique. Symbolic Data Analysis and the SODAS Software, Wiley, pp.373-391, 2008. <inria-00232878>

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