ML Anthology

Robust Boosting for Learning from Few Examples

Lior Wolf, Ian Martin
CVPR 2005 pp. 359-364
doi:10.1109/CVPR.2005.305 /cvpr/2005/wolf2005cvpr-robust/

Abstract

We present and analyze a novel regularization technique based on enhancing our dataset with corrupted copies of our original data. The motivation is that since the learning algorithm lacks information about which parts of the data are reliable, it has to make more robust classification functions. Using this framework, we propose a simple addition to the gentle boosting algorithm which enables it to work with only a few examples. We test this new algorithm on a variety of datasets and show convincing results.

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Cite

Text

Wolf and Martin. "Robust Boosting for Learning from Few Examples." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.305

Markdown

[Wolf and Martin. "Robust Boosting for Learning from Few Examples." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/wolf2005cvpr-robust/) doi:10.1109/CVPR.2005.305

BibTeX

@inproceedings{wolf2005cvpr-robust,
  title     = {{Robust Boosting for Learning from Few Examples}},
  author    = {Wolf, Lior and Martin, Ian},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {359-364},
  doi       = {10.1109/CVPR.2005.305},
  url       = {https://mlanthology.org/cvpr/2005/wolf2005cvpr-robust/}
}