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Mach. Learn. Knowl. Extr. 2018, 1(1), 115-120; https://doi.org/10.3390/make1010006

Why Topology for Machine Learning and Knowledge Extraction?

1
Department of Mathematics, University of Bologna, 40126 Bologna, Italy
2
ARCES, University of Bologna, 40125 Bologna, Italy
Current address: Department of Mathematics, University of Bologna, 40126 Bologna, Italy.
Received: 10 March 2018 / Revised: 26 April 2018 / Accepted: 30 April 2018 / Published: 2 May 2018
(This article belongs to the Section Topology)
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Abstract

Data has shape, and shape is the domain of geometry and in particular of its “free” part, called topology. The aim of this paper is twofold. First, it provides a brief overview of applications of topology to machine learning and knowledge extraction, as well as the motivations thereof. Furthermore, this paper is aimed at promoting cross-talk between the theoretical and applied domains of topology and machine learning research. Such interactions can be beneficial for both the generation of novel theoretical tools and finding cutting-edge practical applications. View Full-Text
Keywords: shape; geometry; topological data analysis; persistence shape; geometry; topological data analysis; persistence
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Ferri, M. Why Topology for Machine Learning and Knowledge Extraction? Mach. Learn. Knowl. Extr. 2018, 1, 115-120.

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