IPBoost – Non-Convex Boosting via Integer Programming
Abstract
Recently non-convex optimization approaches for solving machine learning problems have gained significant attention. In this paper we explore non-convex boosting in classification by means of integer programming and demonstrate real-world practicability of the approach while circumvent- ing shortcomings of convex boosting approaches. We report results that are comparable to or better than the current state-of-the-art.
Cite
Text
Pfetsch and Pokutta. "IPBoost – Non-Convex Boosting via Integer Programming." International Conference on Machine Learning, 2020.Markdown
[Pfetsch and Pokutta. "IPBoost – Non-Convex Boosting via Integer Programming." International Conference on Machine Learning, 2020.](https://mlanthology.org/icml/2020/pfetsch2020icml-ipboost/)BibTeX
@inproceedings{pfetsch2020icml-ipboost,
title = {{IPBoost – Non-Convex Boosting via Integer Programming}},
author = {Pfetsch, Marc and Pokutta, Sebastian},
booktitle = {International Conference on Machine Learning},
year = {2020},
pages = {7663-7672},
volume = {119},
url = {https://mlanthology.org/icml/2020/pfetsch2020icml-ipboost/}
}