A Boosting-Type Convergence Result for AdaBoost.MH with Factorized Multi-Class Classifiers
Xin Zou, Zhengyu Zhou, Jingyuan Xu, Weiwei Liu
Abstract
AdaBoost is a well-known algorithm in boosting. Schapire and Singer propose, an extension of AdaBoost, named AdaBoost.MH, for multi-class classification problems. Kégl shows empirically that AdaBoost.MH works better when the classical one-against-all base classifiers are replaced by factorized base classifiers containing a binary classifier and a vote (or code) vector. However, the factorization makes it much more difficult to provide a convergence result for the factorized version of AdaBoost.MH. Then, Kégl raises an open problem in COLT 2014 to look for a convergence result for the factorized AdaBoost.MH. In this work, we resolve this open problem by presenting a convergence result for AdaBoost.MH with factorized multi-class classifiers.
BibTeX
@inproceedings{
zou2024a,
title={A Boosting-Type Convergence Result for AdaBoost.{MH} with Factorized Multi-Class Classifiers},
author={Xin Zou and Zhengyu Zhou and Jingyuan Xu and Weiwei Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7Lv8zHQWwS}
}