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Apprentissage de Séquences Non-Indépendantes d’Exemples

  • Olivier Teytaud
Conference paper
Part of the Trends in Mathematics book series (TM)

Abstract

Beaucoup de travaux récent considérent les applications pratiques des réseaux neuronaux (ou d’autres algorithmes proches) pour la modélisation de séries temporelles, par exemple chaotiques. Quelques papiers seulement (dont les résultats principaux sont rappelés ici) ont été consacrés aux applications de la partie théorique de l’apprentissage en la matière. Cet article fournit des rappels des résultats basés sur des propriétés d’ergodicité en matière d’apprentissage de suites non-indépendantes d’exemples, puis développe quelques nouveaux résultats.

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Copyright information

© Springer Basel AG 2002

Authors and Affiliations

  • Olivier Teytaud
    • 1
  1. 1.ISCBron CedexFrance

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