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Selective Rademacher Penalization and Reduced Error Pruning of Decision Trees
Journal of Machine Learning Research, (2004): 1107-1126
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Abstract
Rademacher penalization is a modern technique for obtaining data-dependent bounds on the generalization error of classifiers. It appears to be limited to relatively simple hypothesis classes because of computational complexity issues. In this paper we, nevertheless, apply Rademacher penalization to the in practice important hypothesis cla...More
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