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Entropy for non-additive measures in continuous domains
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-0368-8037
2017 (English)In: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 324, p. 49-59Article in journal (Refereed) Published
Abstract [en]

In a recent paper we introduced a definition of f-divergence for non-additive measures. In this paper we use this result to give a definition of entropy for non-additive measures in a continuous setting. It is based on the KL divergence for this type of measures. We prove some properties and show that we can use it to find a measure satisfying the principle of minimum discrimination.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 324, p. 49-59
Keyword [en]
Entropy, KL-divergence, Non-additive measures
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-14115DOI: 10.1016/j.fss.2016.10.001ISI: 000408022500005OAI: oai:DiVA.org:his-14115DiVA: diva2:1141411
Available from: 2017-09-14 Created: 2017-09-14 Last updated: 2018-01-13Bibliographically approved

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Torra, Vicenç

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