Multi-Layer Perceptrons and Symbolic Data
| dc.creator | Rossi, Fabrice | |
| dc.creator | Conan-Guez, Brieuc | |
| dc.date | 2008-02-02 | |
| dc.date.accessioned | 2026-07-07T09:18:27Z | |
| dc.date.available | 2026-07-07T09:18:27Z | |
| dc.description | 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. | |
| dc.identifier | https://arxiv.org/abs/0802.0251 | |
| dc.identifier | http://arxiv.org/abs/0802.0251 | |
| dc.identifier | Symbolic Data Analysis and the SODAS Software Wiley (Ed.) (2008) 373-391 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/154031 | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.title | Multi-Layer Perceptrons and Symbolic Data | |
| dc.type | text |