---
title: "A Simple Additive Re-weighting Strategy for Improving Margins"
authors:
  - "Fabio Aiolli"
  - "Alessandro Sperduti"
doi: ""
url: "https://ijcai.org/proceedings/2001/127"
pdf: "https://ijcai.org/proceedings/2001/0127.pdf"
venue: "Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence"
year: 2001
pages: "927-933"
track: ""
keywords:
---

# A Simple Additive Re-weighting Strategy for Improving Margins

Fabio Aiolli, Alessandro Sperduti

*Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence*, pages 927-933.

## 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. Moreover, the obtained models were able to outperform state of the art algorithms, such as SVM.