Kernels on fuzzy sets: an overview

07/30/2019

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by   Jorge Guevara, et al.

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Universidade de São Paulo

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ibm

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INSA Rouen Normandie

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This paper introduces the concept of kernels on fuzzy sets as a similarity measure for [0,1]-valued functions, a.k.a. membership functions of fuzzy sets. We defined the following classes of kernels: the cross product, the intersection, the non-singleton and the distance-based kernels on fuzzy sets. Applicability of those kernels are on machine learning and data science tasks where uncertainty in data has an ontic or epistemistic interpretation.