04791nam 22006975 450 991073941620332120200701054135.03-030-32853-810.1007/978-3-030-32853-5(CKB)4100000010011882(MiAaPQ)EBC6001860(DE-He213)978-3-030-32853-5(PPN)242819680(EXLCZ)99410000001001188220191226d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierPossibility Theory for the Design of Information Fusion Systems /by Basel Solaiman, Éloi Bossé1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (294 pages)Information Fusion and Data Science,2510-15283-030-32852-X Chapter1: Introduction to possibility theory -- Chapter2: Fundamental possibilistic concepts -- Chapter3: Joint Possibility Distributions and Conditioning -- Chapter4: Possibilistic Similarity Measures -- Chapter5: The interrelated uncertainty modeling theories -- Chapter6: Possibility integral -- Chapter7: Fusion operators and decision-making criteria in the framework of possibility theory -- Chapter8: Possibilistic concepts applied to soft pattern classification -- Chapter9: The use of possibility theory in the design of information fusion systems.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.Information Fusion and Data Science,2510-1528ProbabilitiesStatisticsMathematical statisticsSociophysicsEconophysicsElectrical engineeringProbability Theory and Stochastic Processeshttps://scigraph.springernature.com/ontologies/product-market-codes/M27004Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/S17020Probability and Statistics in Computer Sciencehttps://scigraph.springernature.com/ontologies/product-market-codes/I17036Data-driven Science, Modeling and Theory Buildinghttps://scigraph.springernature.com/ontologies/product-market-codes/P33030Communications Engineering, Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/T24035Probabilities.Statistics.Mathematical statistics.Sociophysics.Econophysics.Electrical engineering.Probability Theory and Stochastic Processes.Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.Probability and Statistics in Computer Science.Data-driven Science, Modeling and Theory Building.Communications Engineering, Networks.511.322Solaiman Baselauthttp://id.loc.gov/vocabulary/relators/aut781688Bossé Éloiauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910739416203321Possibility Theory for the Design of Information Fusion Systems3552896UNINA