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2010

Boosting Multi-Task Weak Learners with Applications to Textual and Social Data

9 years 8 months ago
Boosting Multi-Task Weak Learners with Applications to Textual and Social Data
Abstract--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 [1] 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 hypothesis that tasks behave similarly in the whole learning space. Moreover, MT-Adaboost can learn multiple tasks without imposing the constraint of sharing the same label set and/or examples between tasks. A theoretical analysis is derived from the analysis of the original Adaboost. Experiments for multiple tasks over large scale textual data sets with social context (Enron and Tobacco) give rise to very promising results. Keywords-Multi-task learning; Boosting; Decision trees; Textual and social...
Jean Baptiste Faddoul, Boris Chidlovskii, Fabien T
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICMLA
Authors Jean Baptiste Faddoul, Boris Chidlovskii, Fabien Torre, Rémi Gilleron
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