A Boosting Framework on Grounds of Online Learning
Abstract
By exploiting the duality between boosting and online learning, we present a boosting framework which proves to be extremely powerful thanks to employing the vast knowledge available in the online learning area. Using this framework, we develop various algorithms to address multiple practically and theoretically interesting questions including sparse boosting, smooth-distribution boosting, agnostic learning and, as a by-product, some generalization to double-projection online learning algorithms.
Cite
Text
Mohamadpoor and Pfister. "A Boosting Framework on Grounds of Online Learning." Neural Information Processing Systems, 2014.Markdown
[Mohamadpoor and Pfister. "A Boosting Framework on Grounds of Online Learning." Neural Information Processing Systems, 2014.](https://mlanthology.org/neurips/2014/mohamadpoor2014neurips-boosting/)BibTeX
@inproceedings{mohamadpoor2014neurips-boosting,
title = {{A Boosting Framework on Grounds of Online Learning}},
author = {Mohamadpoor, Tofigh Naghibi and Pfister, Beat},
booktitle = {Neural Information Processing Systems},
year = {2014},
pages = {2267-2275},
url = {https://mlanthology.org/neurips/2014/mohamadpoor2014neurips-boosting/}
}