Akira Maruoka

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Many algorithms for inferring a decision tree from data involve a two-phase process: First, a very large decision tree is grown which typically ends up “over-fitting” the data. To reduce over-fitting, in the second phase, the tree is pruned using one of a number of available methods. The final tree is then output and used for classification on test data. In(More)
Since Razborov, based on the approximation method, succeeded to obtain a superpolynomial lower bound on the size of monotone circuits computing the clique function, much effort has been devoted to explore the method and derive good lower bounds[K, NM, R1, R2, RR]. Employing the approximation method, Alon and Boppana[AB] obtained an exponential lower bound(More)
A classifier with n inputs is a comparator network that classifies a set of n values into two classes with the same number of values in such a way that each value in one class is at least as large as all of those in the other. Based on the utilization of expanders, Pippenger constructed classifiers with n inputs whose size is asymptotic to 2n log n. In the(More)