Density Level Detection is Classification

@inproceedings{Steinwart2004DensityLD,
  title={Density Level Detection is Classification},
  author={Ingo Steinwart and Don R. Hush and Clint Scovel},
  booktitle={NIPS},
  year={2004}
}
We show that anomaly detection can be interpreted as a binary classification problem. Using this interpretation we propose a support vector machine (SVM) for anomaly detection. We then present some theoretical results which include consistency and learning rates. Finally, we experimentally compare our SVM with the standard one-class SVM. 

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