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The problem we address in this paper is to label datapoints when the information about them is provided primarily in terms of their subsets or groups. The knowledge we have for a group is a numerical weight or likelihood value for each group member to belong to same class. These likelihood values are computed given a class specific model, either explicit or implicit, of the pattern we wish to learn. By defining a Conditional Random Field (CRF) over the labels of data, we formulate the problem as an Markov Network inference problem. We present experimental results for analytical model estimation and object localization where the proposed method produces improved performances over other methods.