The Alternating Decision Tree Learning Algorithm
Abstract
1 INTRODUCTION The AdaBoost algorithm [7, 16] has recently proved to be an important component in practical learning algorithms. Two of the most successful combinations have been boosting decision trees and boosting stumps [6, 1, 13, 8]. Stumps are the simplest special case of decision trees which consist of a single decision node and two prediction leaves. Boosting decision trees learning algorithms, such as CART [2] and C4.5 [14], yields very good classifiers.
Cite
Text
Freund and Mason. "The Alternating Decision Tree Learning Algorithm." International Conference on Machine Learning, 1999.Markdown
[Freund and Mason. "The Alternating Decision Tree Learning Algorithm." International Conference on Machine Learning, 1999.](https://mlanthology.org/icml/1999/freund1999icml-alternating/)BibTeX
@inproceedings{freund1999icml-alternating,
title = {{The Alternating Decision Tree Learning Algorithm}},
author = {Freund, Yoav and Mason, Llew},
booktitle = {International Conference on Machine Learning},
year = {1999},
pages = {124-133},
url = {https://mlanthology.org/icml/1999/freund1999icml-alternating/}
}