Linear Programming Boosting via Column Generation

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Linear Programming Boosting via Column Generation
We examine linear program (LP) approaches to boosting and demonstrate their efficient solution using LPBoost, a column generation based simplex method. We formulate the problem as if all possible weak hypotheses had already been generated. The labels produced by the weak hypotheses become the new feature space of the problem. The boosting task becomes to construct a learning function in the label space that minimizes misclassification error and maximizes the soft margin. We prove that for classification, minimizing the 1-norm soft margin error function directly optimizes a generalization error bound. The equivalent linear program can be efficiently solved using column generation techniques developed for large-scale optimization problems. The resulting LPBoost algorithm can be used to solve any LP boosting formulation by iteratively optimizing the dual misclassification costs in a restricted LP and dynamically generating weak hypotheses to make new LP columns. We provide algorithms for...
Ayhan Demiriz, Kristin P. Bennett, John Shawe-Tayl
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 2002
Where ML
Authors Ayhan Demiriz, Kristin P. Bennett, John Shawe-Taylor
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