Density Level Detection is Classification

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Ingo Steinwart, Don Hush, Clint Scovel


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.