Results 261 to 270 of about 313,315 (303)
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Fuzzy measurement of income inequality: a class of fuzzy inequality measures
Social Choice and Welfare, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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IEEE Transactions on Fuzzy Systems, 1994
First, this paper reviews several well known measures of fuzziness for discrete fuzzy sets. Then new multiplicative and additive classes are defined. We show that each class satisfies five well-known axioms for fuzziness measures, and demonstrate that several existing measures are relatives of these classes.
N.R. Pal, J.C. Bezdek
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First, this paper reviews several well known measures of fuzziness for discrete fuzzy sets. Then new multiplicative and additive classes are defined. We show that each class satisfies five well-known axioms for fuzziness measures, and demonstrate that several existing measures are relatives of these classes.
N.R. Pal, J.C. Bezdek
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Fuzzy Sets and Systems, 1981
Abstract In [4] Hohle has defined fuzzy measures on G-fuzzy sets [2] where G stands for a regular Boolean algebra. Consequently, since the unit interval is not complemented, fuzzy sets in the sense of Zadeh [8] do not fit in this framework in a straightforward manner.
Klement, Erich Peter +2 more
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Abstract In [4] Hohle has defined fuzzy measures on G-fuzzy sets [2] where G stands for a regular Boolean algebra. Consequently, since the unit interval is not complemented, fuzzy sets in the sense of Zadeh [8] do not fit in this framework in a straightforward manner.
Klement, Erich Peter +2 more
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International Journal of Intelligent Systems, 2005
Summary: Coherence measures are a tool to compare those fuzzy sets that are sensitive to their own similarity as well as to their fuzzy nature. In this article we find three generalizations of the definition of coherence measures: a first one for any fuzzy set, a second one for any definition of strong negation, and a final one for an extension in ...
Sancho-Royo, A., Verdegay, J. L.
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Summary: Coherence measures are a tool to compare those fuzzy sets that are sensitive to their own similarity as well as to their fuzzy nature. In this article we find three generalizations of the definition of coherence measures: a first one for any fuzzy set, a second one for any definition of strong negation, and a final one for an extension in ...
Sancho-Royo, A., Verdegay, J. L.
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Fuzzy morphology and fuzzy convexity measures
Proceedings of 13th International Conference on Pattern Recognition, 1996This study results in a very general class of approximate convex measures for objects on grey-tone images. These measures are referred to as convexity indicators. They are based on fuzzy set theory, more precisely, on the fuzzy inclusion indicators defined by Sinha and Dougherty (1993). Consideration is given to the fuzzy morphological operations which
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Fuzzy integrals and conditional fuzzy measures
Fuzzy Sets and Systems, 1983We introduce a fuzzy integral for fuzzy events with respect to @l-additive fuzzy measures. This integral is the canonical generalization of the Lehesgue integral. A Radon-Nikodym-like theorem is used to give the definition of the conditional fuzziness.
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On the Fuzzy Measures and the Measures of Fuzziness for L-Fuzzy Sets
IFAC Proceedings Volumes, 1983Abstract This paper is based on the results of De Luca and Termini [1.2] and Wang (6,7,8). It includes the following three parts, (l) We extend the fuzzy integrals taking value in the unit inteval (0,1) to a fuzzy integrals taking value in a lattice.
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2019
This chapter is a key contribution of this work in which various computational approaches to learning fuzzy measures are described. The learning problem is framed from the perspective of data fitting, where we aim to define a model that interpolates or approximates a set of observed or user-specified instances.
Gleb Beliakov +2 more
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This chapter is a key contribution of this work in which various computational approaches to learning fuzzy measures are described. The learning problem is framed from the perspective of data fitting, where we aim to define a model that interpolates or approximates a set of observed or user-specified instances.
Gleb Beliakov +2 more
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2016
Dispersion measures are very useful tools to measure the variability of data. Under uncertainty, the fuzzy set theory can be used to capture the vagueness in the data. This chapter develops the fuzzy versions of classical dispersion measures namely, standard deviation and variance, mean absolute deviation, coefficient of variation, range, and quartiles.
İrem Uçal Sarı +2 more
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Dispersion measures are very useful tools to measure the variability of data. Under uncertainty, the fuzzy set theory can be used to capture the vagueness in the data. This chapter develops the fuzzy versions of classical dispersion measures namely, standard deviation and variance, mean absolute deviation, coefficient of variation, range, and quartiles.
İrem Uçal Sarı +2 more
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Fuzzy Sets and Systems, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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