• G. BLANCHARD
• C. SCHÄFER
• Y. ROZENHOLC

Code(s) de Classification MSC:

• 62H30 Classification and discrimination; cluster analysis [See also 68T10]
• 65N55 Multigrid methods; domain decomposition
• 68Q32 Computational learning theory [See also 68T05]

Résumé: 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.

Mots Clés: Supervised Classification ; Dyadic Tree ; Multi-class Classification ; Misclassification Error Bounds

Date: 2004-03-05

Prépublication numéro: PMA-889