PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Oracle bounds and exact algorithm for dyadic classification trees
Gilles Blanchard, Christin Schaefer and Yves Rozenholc
In: COLT 2004, 1-4 Jul. 2004, Banff, Canada.


This paper introduces a new method using dyadic decision trees for estimating a classification or a regression function in a multiclass classification problem. The estimator is based on model selection by penalized empirical loss minimization. Our work consists in two complementary parts: first, a theoretical analysis of the method leads to deriving oracle-type inequalities for three different possible loss functions. Secondly, we present an algorithm able to compute the estimator in an exact way.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:460
Deposited By:Gilles Blanchard
Deposited On:23 December 2004