Margin-Sparsity Trade-Off for the Set Covering Machine

Abstract

We propose a new learning algorithm for the set covering machine and a tight data-compression risk bound that the learner can use for choosing the appropriate tradeoff between the sparsity of a classifier and the magnitude of its separating margin.

DOI: 10.1007/11564096_23

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Cite this paper

@inproceedings{Laviolette2005MarginSparsityTF, title={Margin-Sparsity Trade-Off for the Set Covering Machine}, author={François Laviolette and Mario Marchand and Mohak Shah}, booktitle={ECML}, year={2005} }