Multi-Layer Perceptrons and Symbolic Data

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.

• Type: Journal
• Year: 2008
• Where: CORR
• Authors: Fabrice Rossi, Brieuc Conan-Guez
• Keywords: CORR 2008, Education, Multilayer Perceptrons, Real World, Real World Situations