Unifying Data Units and Models in Statistics: ...
Type de document :
Communication dans un congrès avec actes
Titre :
Unifying Data Units and Models in Statistics: Focus on (Co-)Clustering
Auteur(s) :
Biernacki, Christophe [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Lourme, Alexandre [Auteur]
Université de Bordeaux [UB]
MOdel for Data Analysis and Learning [MODAL]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Lourme, Alexandre [Auteur]
Université de Bordeaux [UB]
Titre de la manifestation scientifique :
Workshop on Model-based Clustering and Classification (MBC2)
Ville :
Catania
Pays :
Italie
Date de début de la manifestation scientifique :
2016-09-05
Discipline(s) HAL :
Statistiques [stat]/Méthodologie [stat.ME]
Résumé en anglais : [en]
In this talk, we highlight that it is possible to embed data unit selection into a classical model selection principle. We introduce the problem in a regression context before to focus on the model-based clustering and ...
Lire la suite >In this talk, we highlight that it is possible to embed data unit selection into a classical model selection principle. We introduce the problem in a regression context before to focus on the model-based clustering and co-clustering context, for data of different kinds (continuous, categorical).Lire moins >
Lire la suite >In this talk, we highlight that it is possible to embed data unit selection into a classical model selection principle. We introduce the problem in a regression context before to focus on the model-based clustering and co-clustering context, for data of different kinds (continuous, categorical).Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
- slides_Biernacki_Catania2016.pdf
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