Classification Using Margin Pursuit
Abstract
In this work, we study a new approach to optimizing the margin distribution realized by binary classifiers, in which the learner searches the hypothesis space in such a way that a pre-set margin level ends up being a distribution-robust estimator of the margin location. This procedure is easily implemented using gradient descent, and admits finite-sample bounds on the excess risk under unbounded inputs, yielding competitive rates under mild assumptions. Empirical tests on real-world benchmark data reinforce the basic principles highlighted by the theory.
Cite
Text
Holland. "Classification Using Margin Pursuit." Artificial Intelligence and Statistics, 2019.Markdown
[Holland. "Classification Using Margin Pursuit." Artificial Intelligence and Statistics, 2019.](https://mlanthology.org/aistats/2019/holland2019aistats-classification/)BibTeX
@inproceedings{holland2019aistats-classification,
title = {{Classification Using Margin Pursuit}},
author = {Holland, Matthew J.},
booktitle = {Artificial Intelligence and Statistics},
year = {2019},
pages = {712-720},
volume = {89},
url = {https://mlanthology.org/aistats/2019/holland2019aistats-classification/}
}