Margin-Sparsity Trade-Off for the Set Covering Machine
نویسندگان
چکیده
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.
منابع مشابه
A PAC-Bayes approach to the Set Covering Machine
We design a new learning algorithm for the Set Covering Machine from a PAC-Bayes perspective and propose a PAC-Bayes risk bound which is minimized for classifiers achieving a non trivial margin-sparsity trade-off.
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