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Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm, and has quite a few effective implementations… Expand The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in… Expand This article presents an incremental algorithm for inducing decision trees equivalent to those formed by Quinlan's nonincremental… Expand Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a… Expand Many interesting computational problems can be reformulated in terms of decision trees. A natural classical algorithm is to then… Expand Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classification tasks even when the… Expand In this paper, a new probabilistic tagging method is presented which avoids problems that Markov Model based taggers face, when… Expand This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill… Expand A survey is presented of current methods for decision tree classifier (DTC) designs and the various existing issues. After… Expand We demonstrate that cons&g optimal binary de&ion trees ia an NP=compkt.e probtem, where an op timal tree is one which minin&s the… Expand