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Parameter Learning in Probabilistic Databases: A Least Squares Approach

Parameter Learning in Probabilistic Databases: A Least Squares Approach

This video was recorded at 6th International Workshop on Mining and Learning with Graphs (MLG), Helsinki 2008. Probabilistic databases compute the success probabilities of queries. We introduce the problem of learning the parameters of the probabilistic database ProbLog. Given the observed success probabilities of a set of queries, we compute the probabilities attached to facts that have a low approximation error on the training data as well as on unseen examples. Assuming Gaussian error terms on the observed success probabilities, this naturally leads to a least squares optimization problem. Experiments on real world data show the usefulness and effectiveness of this least squares calibration of probabilistic databases.

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