ML Anthology

Oracle Bounds and Exact Algorithm for Dyadic Classification Trees

Gilles Blanchard, Christin Schäfer, Yves Rozenholc
COLT 2004 pp. 378-392
doi:10.1007/978-3-540-27819-1_26 /colt/2004/blanchard2004colt-oracle/

Abstract

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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Text

Blanchard et al. "Oracle Bounds and Exact Algorithm for Dyadic Classification Trees." Annual Conference on Computational Learning Theory, 2004. doi:10.1007/978-3-540-27819-1_26

Markdown

[Blanchard et al. "Oracle Bounds and Exact Algorithm for Dyadic Classification Trees." Annual Conference on Computational Learning Theory, 2004.](https://mlanthology.org/colt/2004/blanchard2004colt-oracle/) doi:10.1007/978-3-540-27819-1_26

BibTeX

@inproceedings{blanchard2004colt-oracle,
  title     = {{Oracle Bounds and Exact Algorithm for Dyadic Classification Trees}},
  author    = {Blanchard, Gilles and Schäfer, Christin and Rozenholc, Yves},
  booktitle = {Annual Conference on Computational Learning Theory},
  year      = {2004},
  pages     = {378-392},
  doi       = {10.1007/978-3-540-27819-1_26},
  url       = {https://mlanthology.org/colt/2004/blanchard2004colt-oracle/}
}