Real Boosting a La Carte with an Application to Boosting Oblique Decision Tree
Abstract
In the past ten years, boosting has become a major field of machine learning and classification. This paper brings contributions to its theory and algorithms. We first unify a well-known top-down decision tree induction algorithm due to Kearns and Mansour, and discrete AdaBoost, as two versions of a same higher-level boosting algorithm. It may be used as the basic building block to devise simple provable boosting algorithms for complex classifiers. We provide one example: the first boosting algorithm for Oblique Decision Trees, an algorithm which turns out to be simpler, faster and significantly more accurate than previous approaches. URL: http://www.univ-ag.fr/~rnock/Articles/Drafts/ijcai07-hnn.pdf
Cite
Text
Henry et al. "Real Boosting a La Carte with an Application to Boosting Oblique Decision Tree." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Henry et al. "Real Boosting a La Carte with an Application to Boosting Oblique Decision Tree." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/henry2007ijcai-real/)BibTeX
@inproceedings{henry2007ijcai-real,
title = {{Real Boosting a La Carte with an Application to Boosting Oblique Decision Tree}},
author = {Henry, Claudia and Nock, Richard and Nielsen, Frank},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {842-847},
url = {https://mlanthology.org/ijcai/2007/henry2007ijcai-real/}
}