A Simple Additive Re-weighting Strategy for Improving Margins
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.
@inproceedings{ijcai2001p127,
title = {A Simple Additive Re-weighting Strategy for Improving Margins},
author = {Aiolli, Fabio and Sperduti, Alessandro},
booktitle = {Proceedings of the Seventeenth International Joint Conference on
Artificial Intelligence, {IJCAI-01}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {927--933},
year = {2001},
url = {https://ijcai.org/proceedings/2001/127},
}
TY - CPAPER TI - A Simple Additive Re-weighting Strategy for Improving Margins AU - Aiolli, Fabio AU - Sperduti, Alessandro T2 - Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI-01 PB - International Joint Conferences on Artificial Intelligence PY - 2001 SP - 927 EP - 933 UR - https://ijcai.org/proceedings/2001/127 ER -Copied
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