Titlebook: Recent Developments in Fuzzy Logic and Fuzzy Sets; Dedicated to Lotfi A Shahnaz N. Shahbazova,Michio Sugeno,Janusz Kacprzy Book 2020 Spring

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Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explaainty-based formalization of what is clustering automatically leads to the mathematical ideas and definitions behind this algorithm. Thus, we provide an explanation for this algorithm’s empirical success.

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Semi-supervised Learning to Rank with Nonlinear Preference Model,ng function. In order to reduce dimension of the problem, ranking learning problem on clusters in feature space is stated, while aggregated training dataset consist of estimates of clusters centers and average rank of the items inside each cluster. The problem of both linear and nonlinear models of

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A Calculation Model of Hierarchical Multiplex Structure with Fuzzy Inference for VRSD Problem,tween conventional methods and complex situations in the real world. A HIMS model (a calculation model with .erarchical .ultiplex .tructure), is also proposed for the VRSD problem. It contains 3 levels: Atom level (system energies can be controlled by a heuristic method), Molecular level (system pro