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Fuzzy Measure Theory

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TLDR
Introduction.
Abstract
Introduction. Required Background in Set Theory. Fuzzy Measures. Extensions. Structural Characteristics for Set Functions. Measurable Functions on Fuzzy Measure Spaces. Fuzzy Integrals. PanIntegrals. Applications. Index.

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Proceedings Article

Sets of Joint Probability Measures Generated by Weighted Marginal Focal Sets

TL;DR: This paper is devoted to the construction of sets of joint probability measures for the case that the marginal sets of probability measures are generated by weighted focal sets.
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A generalized framework for mean aggregation: Toward the modeling of cognitive aspects

TL;DR: An overview of mean/averaging operators is provided and a generalized formulation using a fuzzy measure to convey information about the importances of the different arguments in the aggregation is provided.
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The key theorem and the bounds on the rate of uniform convergence of learning theory on Sugeno measure space

TL;DR: In this article, the key theorem of learning theory, the bounds on the rate of convergence of learning process and the relations between these bounds and capacity of the set of functions on Sugeno measure space are given.
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Some geometric Choquet aggregation operators using Einstein operations under intuitionistic fuzzy environment

TL;DR: This paper investigates the methods of dealing with the information aggregation problems in the context of intuitionistic fuzzy set and interval-valued intuitionistic fuzziness set respectively and proposes the Einstein based intuitionists fuzzy Choquet geometric EIFCG operator and Einstein based interval- valued Choquet geometry EIIFCG operators.
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Set Measure Directed Multi-Source Information Fusion

TL;DR: This work investigates the use of a monotonic set measure as a means of representing the fusion imperative in the multi-source fusion problem, and looks at the fusion of various different types of information, precise data, uncertain information such as probabilistic and possibilistic.