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Possibility Theory for the Design of Information Fusion Systems


Menge:  Stück  
Produktinformationen
cover
cover
Artikel-Nr.:
     5667A-9783030328559
Hersteller:
     Springer Verlag
Herst.-Nr.:
     9783030328559
EAN/GTIN:
     9783030328559
Suchbegriffe:
Mathematik-Bücher
Mathematikbücher - englischsprachig
mathematik bücher
This practical guidebook describes the basic concepts, the mathematical developments, and the engineering methodologies for exploiting possibility theory for the computer-based design of an information fusion system where the goal is decision support for industries in smart ICT (information and communications technologies).  This exploitation of possibility theory improves upon probability theory, complements Dempster-Shafer theory, and fills an important gap in this era of Big Data and Internet of Things.The book discusses fundamental possibilistic concepts: distribution, necessity measure, possibility measure, joint distribution, conditioning, distances, similarity measures, possibilistic decisions, fuzzy sets, fuzzy measures and integrals, and finally, the interrelated theories of uncertainty..uncertainty. These topics form an essential tour of the mathematical tools needed for the latter chapters of the book. These chapters present applications related to  decision-making and pattern recognition schemes, and finally, a concluding chapter on the use of possibility theory in the overall challenging design of an information fusion system. This book will appeal to researchers and professionals in the field of information fusion and analytics, information and knowledge processing, smart ICT, and decision support systems.
Weitere Informationen:
Author:
Basel Solaiman; Éloi Bossé
Verlag:
Springer International Publishing
Sprache:
eng
Weitere Suchbegriffe: possibility distribution models, imprecise type possibility distribution, possibility and necessity measures, possibilistic decision making, marginal possibility distributions, fuzzy measures and integrals, possibilistic similarity measures, possibilistic maximum likelihood, data-driven science, modeling and theory building
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