Multi-Layer Perceptrons and Symbolic Data
Neural and Evolutionary Computing
2008-02-05 v1
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.
Cite
@article{arxiv.0802.0251,
title = {Multi-Layer Perceptrons and Symbolic Data},
author = {Fabrice Rossi and Brieuc Conan-Guez},
journal= {arXiv preprint arXiv:0802.0251},
year = {2008}
}