Density Level Detection Is Classification
Abstract
We show that anomaly detection can be interpreted as a binary classifi- cation problem. Using this interpretation we propose a support vector machine (SVM) for anomaly detection. We then present some theoret- ical results which include consistency and learning rates. Finally, we experimentally compare our SVM with the standard one-class SVM.
Cite
Text
Steinwart et al. "Density Level Detection Is Classification." Neural Information Processing Systems, 2004.Markdown
[Steinwart et al. "Density Level Detection Is Classification." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/steinwart2004neurips-density/)BibTeX
@inproceedings{steinwart2004neurips-density,
title = {{Density Level Detection Is Classification}},
author = {Steinwart, Ingo and Hush, Don and Scovel, Clint},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1337-1344},
url = {https://mlanthology.org/neurips/2004/steinwart2004neurips-density/}
}