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Fuzzy Measure Theory
Zhenyuan Wang,George J. Klir +1 more
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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.read more
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Proceedings ArticleDOI
A neural architecture for fuzzy classification with application to complex system tracking
P.A. Stadter,A.K. Garga +1 more
TL;DR: The application of a new architecture for fuzzy pattern classification relies upon the integration of fuzzy logic techniques with an artificial neural architecture to produce an efficient mechanism for classifying input patterns.
Book ChapterDOI
Rough fuzzy integrals for information fusion and classification
Tao Guan,Boqin Feng +1 more
TL;DR: These types of integrals generalize fuzzy integrals and enlarge their domains of applications in fusion and classification under rough uncertainty and show that they fuse or classify objects with rough features with fairly good effects while the existed methods can not solve.
Posted Content
Plausibility Measures and Default Reasoning
Nir Friedman,Joseph Y. Halpern +1 more
TL;DR: In this article, the authors introduce a new approach to modeling uncertainty based on plausibility measures, which is easily seen to generalize other approaches to modelling uncertainty, such as probability measures, belief functions, and possibility measures.
Book ChapterDOI
An Object-Oriented Architecture for Possibilistic Models
TL;DR: An architecture for the implementation of possibilistic models in an object-oriented programming environment (C++ in particular) is described and Supplementary methods—including the fast Mobius transform, the maximum entropy and Bayesian approximations of random sets, distribution operators, compatibility measures, consonant approximation, frequency conversions, and possibillistic normalization and measurement methods are introduced.
Journal ArticleDOI
Using Non-Additive Measure for Optimization-Based Nonlinear Classification
TL;DR: The research produces practically deliverable nonlinear models with the non-additive measure for classification problem in data mining when interactions among attributes are considered and applies non- additive measures on classic optimization-based models.