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Learning multiple related tasks from data simultaneously can improve predictive performance relative to learning these tasks independently. In this paper we propose a novel multi-task learning algorithm called MT-Adaboost: it extends Ada boost algorithm  to the multi-task setting; it uses as multi-task weak classifier a multi-task decision stump. This allows to learn different dependencies between tasks for different regions of the learning space. Thus, we relax the conventional hypothesisdoi:10.1109/icmla.2010.61 dblp:conf/icmla/FaddoulCTG10 fatcat:wo7lsfz27nbhlabzzeiyb3bz4m