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Efficient SVDD sampling with approximation guarantees for the decision boundary |
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| Authors: | Englhardt Adrian Trittenbach Holger Kottke Daniel Sick Bernhard Böhm Klemens |
| Affiliation: | 1.Karlsruhe Institute of Technology, Karlsruhe, Germany ;2.University of Kassel, Kassel, Germany ; |
| Abstract: | Machine Learning - Support Vector Data Description (SVDD) is a popular one-class classifier for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data... |
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