A Theory of Multiclass Boosting
Abstract
Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the “correct” requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we create a broad and general framework, within which we make precise and identify the optimal requirements on the weak-classifier, as well as design the most effective, in a certain sense, boosting algorithms that assume such requirements.
Cite
Text
Mukherjee and Schapire. "A Theory of Multiclass Boosting." Journal of Machine Learning Research, 2013.Markdown
[Mukherjee and Schapire. "A Theory of Multiclass Boosting." Journal of Machine Learning Research, 2013.](https://mlanthology.org/jmlr/2013/mukherjee2013jmlr-theory/)BibTeX
@article{mukherjee2013jmlr-theory,
title = {{A Theory of Multiclass Boosting}},
author = {Mukherjee, Indraneel and Schapire, Robert E.},
journal = {Journal of Machine Learning Research},
year = {2013},
pages = {437-497},
volume = {14},
url = {https://mlanthology.org/jmlr/2013/mukherjee2013jmlr-theory/}
}