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Novelty Discovery with Kernel Minimum Enclosing Balls

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12096)

Abstract

We introduce the idea of utilizing ensembles of Kernel Minimum Enclosing Balls to detect novel datapoints. To this end, we propose a novelty scoring methodology that is based on combining outcomes of the corresponding characteristic functions of a set of fitted balls. We empirically evaluate our model by presenting experiments on synthetic as well as real world datasets.

Supported by the Competence Center for Machine Learning Rhine Ruhr (ML2R) which is funded by the Federal Ministry of Education and Research of Germany (grant no. 01|S18038A).

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  • DOI: 10.1007/978-3-030-53552-0_37
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Fig. 1.

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Correspondence to Rafet Sifa .

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Sifa, R., Bauckhage, C. (2020). Novelty Discovery with Kernel Minimum Enclosing Balls. In: Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2020. Lecture Notes in Computer Science(), vol 12096. Springer, Cham. https://doi.org/10.1007/978-3-030-53552-0_37

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