Abstract :
[en] We consider randomization schemes of the Chow-Liu algorithm from weak (bagging, of quadratic complexity) to strong ones (full random sampling, of linear complexity), for learning probability density models in the form of mixtures of Markov trees. Our empirical study on high-dimensional synthetic problems shows that, while bagging is the most accurate scheme on average, some of the stronger randomizations remain very competitive in terms of accuracy, specially for small sample sizes.
Funders :
FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture [BE]
Wallonie-Bruxelles International, FNRS, Ministère Français des Affaires étrangères et Européennes, Ministère français de l'Enseignement supérieur et de la Recherche dans le cadre des partenariats Hubert Curien
Biomagnet IUAP network of the Belgian Science Policy Office
Pascal2 network of excellence of the EC
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