Density Level Detection is Classification

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.