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Scripts and notebooks to reproduce the experiments and analyses of the paper Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm, "Efficient SVDD sampling with approximation guarantees for the decision boundary", Machine Learning (2022).

Home Page: https://www.ipd.kit.edu/ocs/

License: MIT License

Julia 2.72% Jupyter Notebook 96.64% Python 0.48% Shell 0.17%
anomaly-detection classification data-reduction machine-learning one-class outlier-detection sampling-strategies support-vector-data-description svdd

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