Kernels on fuzzy sets: an overview
Machine Learning
2019-07-31 v1 Artificial Intelligence
Machine Learning
Abstract
This paper introduces the concept of kernels on fuzzy sets as a similarity measure for -valued functions, a.k.a. \emph{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.
Keywords
Cite
@article{arxiv.1907.12991,
title = {Kernels on fuzzy sets: an overview},
author = {Jorge Guevara and Roberto Hirata and Stéphane Canu},
journal= {arXiv preprint arXiv:1907.12991},
year = {2019}
}
Comments
Learning on Distributions, Functions, Graphs and Groups @ NIPS-2017, 8th Dec