, Approche du mélange de filtres pour la sélection de mesures dépendant du contexte. L'illustration est donnée dans le cas d'un vecteur d'observation de dimension 2, qui résulte donc en l'exploitation de 3 filtres distincts exploitant respectivement les mesures y 1
, apprentissage ne nécessite ici de définir que les paramètres du réseau de gating. Pour des raisons de simplicité, nous exploitons ici un modèle de région d'activation basé sur des noyaux gaussiens uni-modaux. Ce choix nous permet notamment d'exploiter l'algorithme Expectation Maximisation (EM), 2013.
, pour optimiser les paramètres du modèle, dans le cas du mélange d'experts comme du mélange de filtres , nous allons chercheràchercherà expliquer au mieux les données d'apprentissage constituées de vecteurs d'observation et de l'´ etat associé. Par conséquent, durant l'apprentissage, nous allons chercheràchercherà expliquer au mieux l'´ etat x t au travers deséquationsdeséquations de filtrage, ce qui n'est pas le cas dans le cadre de l'apprentissage génératif traditionnel, qui cherche luì a optimiser les paramètres du modèlé etat observation demanì erè a expliquer au mieux l'´ evolution de l'´ etatàetatà travers la distribution de prédiction p(x t |x t?1 ) ainsi que les observationsàobservationsà travers la distribution d'observation p(y t |x t ) Notons que cela signifie donc que leséquationsleséquations de filtrage ne sont donc pas exploitées dans le cas de l'apprentissage génératif Discriminative training of kalman filters, Cette spécificité a des conséquences importantes en terme de robustesse, Bibliography Pieter Abbeel Proceedings of Robotics: Science and Systems, 2005.
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