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Updated: Jun 27, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
Some Theoretical Foundations of Bare-Simulation Optimization of Some Directed Distances between Fuzzy Sets
Michel Broniatowski1, Wolfgang Stummer2
1Laboratoire de Probabilités, Statistique et Modélisation, Sorbonne Université, 4 Place Jussieu, 75252 Paris, France.
Abstract:
It is well known that in information theory-as well as in the adjacent fields of statistics, machine learning and artificial intelligence-it is essential to quantify the dissimilarity between objects of uncertain/imprecise/inexact/vague information; correspondingly, constrained optimization is of great importance, too. In view of this, we define the dissimilarity-measure-natured generalized φ-divergences between fuzzy sets, ν-rung orthopair fuzzy sets, extended representation type ν-rung orthopair fuzzy sets as well as between those fuzzy set types and vectors. For those, we present how to tackle corresponding constrained minimization problems by appropriately applying our recently developed dimension-free bare (pure) simulation method. An analogous program is carried out by defining and optimizing generalized φ-divergences between (rescaled) basic belief assignments as well as between (rescaled) basic belief assignments and vectors.
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