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We consider multisplitting of numerical value ranges, a task that is encountered as a discretization step preceding induction and also embedded into learning algorithms. We are interested in finding the partition that optimizes the value of a given attribute evaluation function. For most commonly used evaluation functions this task takes quadratic time in(More)
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 penaliza-tion to the in practice important hypothesis class of unrestricted(More)
The complexity of numerical domain partitioning depends on the number of potential cut points. In multiway partitioning this dependency is often quadratic, even exponential. Therefore , reducing the number of candidate cut points is important. For a large family of attribute evaluation functions only boundary points need to be considered as candidates. We(More)