rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Possibility Theory for the Design of Information Fusion Systems
  name:
    name part: Solaiman, Basel.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Bossé, Éloi.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 288 p. 122 illus., 87 illus. in color. online resource.
  abstract: 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.
  table of contents: 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.
  note: by Basel Solaiman, Éloi Bossé.
  subject:
    topic: Probabilities
  subject:
    topic: Statistics
  subject:
    topic: Mathematical statistics
  subject:
    topic: Sociophysics
  subject:
    topic: Econophysics
  subject:
    topic: Electrical engineering
  subject:
    topic: Probability Theory and Stochastic Processes
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  subject:
    topic: Probability and Statistics in Computer Science
  subject:
    topic: Data-driven Science, Modeling and Theory Building
  subject:
    topic: Communications Engineering, Networks
  classification: QA273.A1-274.9
  classification: QA274-274.9
  classification: 519.2
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Information Fusion and Data Science
  identifier: 9783030328535
  identifier: https://doi.org/10.1007/978-3-030-32853-5
  record info:
    record creation date: 191226
    record change date: 20191226053120.0
    record identifier: 978-3-030-32853-5