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LEARNING HYPERPLANES THAT CAPTURES THE GEOMETRIC STRUCTURE OF CLASS REGIONS
2013
Graduate Research in Engineering and Technology
Most of the decision tree algorithms rely on impurity measures to evaluate the goodness of hyperplanes at each node while learning a decision tree in a top-down fashion. These impurity measures are not differentiable with relation to the hyperplane parameters. Therefore the algorithms for decision tree learning using impurity measures need to use some search techniques for finding the best hyperplane at every node. These impurity measures don't properly capture the geometric structures of the
doi:10.47893/gret.2013.1003
fatcat:y2qmaqxbunbi5ntipufnlujoaa