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Thèse de

Jean-Baptiste Faddoul

lundi 18 juin 2012
Université de Lille 3

Ensemble Methods to Learn Multiple Heterogenous Tasks without Restrictions

Directeur de Thèse : Rémi Gilleron, professeur des universités

Learning multiple related tasks jointly by exploiting their underlying shared knowledge can improve the predictive performance on every
task compared to learning them individually. In this thesis, we address the problem of multi-task learning (MTL) when the tasks are
heterogenous: they do not share the same labels (eventually with different number of labels), they do not require shared examples. In
addition, no prior assumption about the relatedness pattern between tasks is made.
Our contribution to multi-task learning lies in the framework of ensemble learning where the learned function consists normally of an
ensemble of “weak ” hypothesis aggregated together by an ensemble learning algorithm (Boosting, Bagging, etc.). We propose two ap-
proaches to cope with heterogenous tasks without making prior assumptions about the relatedness patterns. For each approach, we
devise novel multi-task weak hypothesis along with their learning algorithms then we adapt a boosting algorithm to the multi-task setting.


UMR 8022 - Laboratoire d'Informatique Fondamentale de Lille - Copyright © 2012 Sophie TISON - Crédits & Mentions légales

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