nips nips2004 nips2004-49 nips2004-49-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Ingo Steinwart, Don Hush, Clint Scovel
Abstract: 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. 1
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