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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)
Top-down induction of decision trees has been observed to suffer from the inadequate functioning of the pruning phase. In particular, it is known that the size of the resulting tree grows linearly with the sample size, even though the accuracy of the tree does not improve. Reduced Error Pruning is an algorithm that has been used as a representative(More)
Handling continuous attribute ranges remains a deeciency of top-down induction of decision trees. They require special treatment and do not t the learning scheme as well as one could hope for. Nevertheless, they are common in practical tasks and, therefore, need to be taken into account. This topic has attracted abundant attention in recent years. 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)