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Efficient max-margin Markov learning via conditional gradient and probabilistic inference
This video was recorded at Open House on Multi-Task and Complex Outputs Learning, London 2006. We present a general and efficient optimisation methodology for for max-margin sructured classification tasks. The efficiency of the method relies on the interplay of several techiques: marginalization of the dual of the structured SVM, or max-margin Markov problem; partial decomposition via a gradient formulation; and finally tight coupling of a max-likelihood inference algorithm into the optimization algorithm, as opposed to using inference as a working set maintenance mechanism only.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
