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Efficient Training of Structured SVMs via Soft Constraints
2015
International Conference on Artificial Intelligence and Statistics
Structured output prediction is a powerful framework for jointly predicting interdependent output labels. Learning the parameters of structured predictors is a central task in machine learning applications. However, training the model from data often becomes computationally expensive. Several methods have been proposed to exploit the model structure, or decomposition, in order to obtain e cient training algorithms. In particular, methods based on linear programming relaxation, or dual
dblp:conf/aistats/MeshiSH15
fatcat:bvktqsanxzgc7m633ia2ix6wlm