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.
CITATION STYLE
Blanchard, G., Schäfer, C., & Rozenholc, Y. (2004). Oracle bounds and exact algorithm for dyadic classification trees. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3120, pp. 378–392). Springer Verlag. https://doi.org/10.1007/978-3-540-27819-1_26
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