, ? NousétudionsNousétudions la performance des algorithmes d'apprentissage via une minimisation empirique du risque. Il estàestà noter que l'analyse de l'algorithme ERM et des propriétés des algorithmes d'apprentissage statistique sont desprobì emes très importants et urgentsàgentsà résoudre
, ? NousétudionsNousétudions les propriétés de généralisation d'un algorithme d'apprentissage statistique sélectionné
, estimation de MV-set peutêtrepeutêtre transposée dans un cadre d'apprentissage très semblablè a la minimisation empirique du risque dans la classification (supervisée), vol.126
, Dans toute cette section, I A est la fonction indicatrice de l'´ evénement A. On suppose que X = (X n ) n?N est une cha??necha??ne de Markov homogène sur un espace d'´ etat général (E, E) avec probabilité de transition ? et loi initiale ?. Le lecteur pourra noter que Fevrier 2016, Présentation au Workshop COmputational Statistics and MOlecular Simulation, Le lecteur intéressé trouvera dans la littérature susmentionnée les preuves des théorèmes théorèmesénoncés pour des cha??nescha??nes de MarkovàMarkovà espace d'´ etat général, 2016.
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