Cartes auto-organisées pour l'analyse exploratoire de données et la visualisation

Article de synthèse sur les applications de l'algorithme de Kohonen pour la visualisation et l'analyse de donnéesThis paper shows how to use the Kohonen algorithm to represent multidimensional data, by exploiting the self-organizing property. It is possible to get such maps as well for quantitative variables as for qualitative ones, or for a mixing of both. The contents of the paper come from various works by SAMOS-MATISSE members, in particular by E. de Bodt, B. Girard, P. Letrémy, S. Ibbou, P. Rousset. Most of the examples have been studied with the computation routines written by Patrick Letrémy, with the language IML-SAS, which are available on the WEB page http://samos.univ-paris1.fr

Modèles de réseaux de neurones pour l'analyse des séries temporelles ou la régression: Estimation, identification, méthode d'élagage SSM

This paper deals with neural network modeling for time series analysis or regression. Based on recent results about the least-square estimation for non-linear time series, we propose a complete and feasible methodology for both parameter estimation (learning process) and model selection (architecture selection). In particular, we solve the pruning problem for multilayer perceptron models with a stepwise search method by using a BIC criterion which is proved to be consistent.link_to_subscribed_fulltex