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Prototypes within Minimum Enclosing Balls

: Bauckhage, Christian; Sifa, Rafet; Dong, Tiansi


Tetko, I.V.:
Artificial Neural Networks and Machine Learning - ICANN 2019. Workshop and Special Sessions. Proceedings : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11731)
ISBN: 978-3-030-30492-8 (Print)
ISBN: 978-3-030-30493-5 (Online)
International Conference on Artificial Neural Networks (ICANN) <28, 2019, Munich>
Conference Paper
Fraunhofer IAIS ()

We revisit the kernel minimum enclosing ball problem and show that it can be solved using simple recurrent neural networks. Once solved, the interior of a ball can be characterized in terms of a function of a set of support vectors and local minima of this function can be thought of as prototypes of the data at hand. For Gaussian kernels, these minima can be naturally found via a mean shift procedure and thus via another recurrent neurocomputing process. Practical results demonstrate that prototypes found this way are descriptive, meaningful, and interpretable.