Apprentissage supervisé de données symboliques et l'adaptation aux données massives et distribuées

Apprentissage supervisé de données symboliques et l'adaptation aux données massives et distribuées

Cette thèse a pour but l'enrichissement des méthodes supervisées d'analyse de données symboliques et l'extension de ce domaine aux données volumineuses, dites "Big Data". Nous proposons à cette fin une méthode supervisée nommée HistSyr. HistSyr convertit automatiquement les...

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Bibliographic Details
Main Author:	Haddad, Raja
Other Authors:	Paris Sciences et Lettres
Language:	fr
Published:	2016
Subjects:	Analyse de Données Symboliques (ADS) Histogrammes Arbres de décision symboliques Big Data Map/Reduce Hadoop Amazon Web Services Symbolic Data Analysis (SDA) Histograms Symbolic decision trees 005.7
Online Access:	http://www.theses.fr/2016PSLED028/document

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