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Training processes of structured prediction models such as structural SVMs involve frequent computations of the maximum-a-posteriori (MAP) prediction given a parameterized model. For specific output structures such as sequences or trees, MAP estimates can be computed efficiently by dynamic programming algorithms such as the Viterbi algorithm and the CKY… (More)

Interdependent training instances violate the common assumption of independently drawn examples and render classical learning algorithms an inappropriate choice. Collective inference approaches explicitly incorporate these dependencies by translating the examples into a graph where two training instances are connected if their values depend on each other.… (More)

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