PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Optimal Dyadic Decision Trees
Gilles Blanchard, Christin Schaefer, Yves Rozenholc and Klaus-Robert Müller
Machine Learning Volume 66, Number 2-3, pp. 209-242, 2007.

Abstract

We introduce a novel algorithm for optimal dyadic decision trees (ODT). The method combines guaranteed performance in the learning theoretical sense and optimal search from the algorithmic point of view. Furthermore it inherits the explanatory power of tree approaches, while not losing too much classification accuracy when compared to advanced kernel based learning methods. Experiments on artificial and benchmark data underline the versatility and quality of our new method.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1324
Deposited By:Gilles Blanchard
Deposited On:28 November 2005