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This paper addresses the inference of probabilistic classification models using weakly supervised learning. The main contribution of this work is the development of learning methods for training datasets consisting of groups of objects with known relative class priors. This can be regarded as a generalization of the situation addressed by Bishop and Ulusoy (2005) , where training information is given as the presence or absence of object classes in each set. Generative and discriminativedoi:10.1016/j.patrec.2010.10.001 fatcat:cicrhinhkzfqvlpvvb6bcs2x3u