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.