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Mining for the Most Certain Predictions from Dyadic Data

Mining for the Most Certain Predictions from Dyadic Data

This video was recorded at 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Paris 2009. In several applications involving regression or classification, along with making predictions it is important to assess how accurate or reliable individual predictions are. This is particularly important in cases where due to finite resources or domain requirements, one wants to make decisions based only on the most reliable rather than on the entire set of predictions. This paper introduces novel and effective ways of ranking predictions by their accuracy for problems involving large-scale, heterogeneous data with a dyadic structure, i.e., where the independent variables can be naturally decomposed into three groups associated with two sets of elements and their... Show More

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