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On the f-divergence for non-additive measures
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. IIIA-CSIC, Campus UAB s/n, Bellaterra, Catalonia, Spain. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-0368-8037
Toho Gakuen, Kunitachi, Tokyo, Japan.
ECSC, c/ Gonzalo Gutiérrez Quirós s/n, Mieres, Spain.
2016 (English)In: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 292, 364-379 p.Article in journal (Refereed) Published
Abstract [en]

The f -divergence evaluates the dissimilarity between two probability distributions defined in terms of the Radon–Nikodym derivative of these two probabilities. The f -divergence generalizes the Hellinger distance and the Kullback–Leibler divergence among other divergence functions. In this paper we define an analogous function for non-additive measures. We discuss them for distorted Lebesgue measures and give examples. Examples focus on the Hellinger distance.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 292, 364-379 p.
Keyword [en]
Hellinger distance, Non-additive measures, Fuzzy measures, Radon–Nikodym derivative, Choquet integral, Capacities
National Category
Computer Systems
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-13064DOI: 10.1016/j.fss.2015.07.006ISI: 000371786900024Scopus ID: 2-s2.0-84938236463OAI: oai:DiVA.org:his-13064DiVA: diva2:1044077
Available from: 2016-11-01 Created: 2016-11-01 Last updated: 2017-11-29Bibliographically approved

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Torra, Vicenç

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