A Simple Additive Re-Weighting Strategy for Improving Margins
Abstract
We present a sample re-weighting scheme inspired by recent results in margin theory. The basic idea is to add to the training set replicas of samples which are not classified with a sufficient margin. We prove the convergence of the input distribution obtained in this way. As study case, we consider an instance of the scheme involving a 1-NN classifier implementing a Vector Quantization algorithm that accommodates tangent distance models. The tangent distance models created in this way have shown a significant improvement in generalization power with respect to the standard tangent models. More-over, the obtained models were able to outperform state of the art algorithms, such as SVM.
Cite
Text
Aiolli and Sperduti. "A Simple Additive Re-Weighting Strategy for Improving Margins." International Joint Conference on Artificial Intelligence, 2001.Markdown
[Aiolli and Sperduti. "A Simple Additive Re-Weighting Strategy for Improving Margins." International Joint Conference on Artificial Intelligence, 2001.](https://mlanthology.org/ijcai/2001/aiolli2001ijcai-simple/)BibTeX
@inproceedings{aiolli2001ijcai-simple,
title = {{A Simple Additive Re-Weighting Strategy for Improving Margins}},
author = {Aiolli, Fabio and Sperduti, Alessandro},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2001},
pages = {927-934},
url = {https://mlanthology.org/ijcai/2001/aiolli2001ijcai-simple/}
}