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We develop an algorithm for structured prediction with nondecomposable performance measures. The algorithm learns parameters of Markov Random Fields (MRFs) and can be applied to multivariate performance measures. Examples include performance measures such as $(F_{\beta })$ score (natural language processing), intersection over union (object category segmentation), Precision/Recall at k (search engines),...
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