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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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Contributor : Fabrice Rossi Connect in order to contact the contributor
Submitted on : Saturday, February 2, 2008 - 3:50:55 PM
Last modification on : Friday, February 4, 2022 - 3:09:38 AM
Long-term archiving on: : Monday, May 3, 2010 - 4:11:00 PM


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


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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