Advances in the Dempster-Shafer theory of evidence
Author(s)
Bibliographic Information
Advances in the Dempster-Shafer theory of evidence
J. Wiley, c1994
Available at 17 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references and index
Description and Table of Contents
Description
Builds on classical probability theory and offers an extremely workable solution to the many problems of artificial intelligence, concentrating on the rapidly growing areas of fuzzy reasoning and neural computing. Contains a collection of previously unpublished articles by leading researchers in the field.
Table of Contents
Partial table of contents: DEMPSTER-SHAFER THEORY OF EVIDENCE: GENERAL ISSUES. Measures of Uncertainty in the Dempster-Shafer Theory of Evidence (G. Klir). Comparative Beliefs (S. Wong, et al.). Calculus with Linguistic Probabilities and Beliefs (M. Lamata & S. Moral). FUZZIFICATION OF DEMPSTER-SHAFER THEORY OF EVIDENCE. Rough Membership Functions (Z. Pawlak & A. Skowron). DEMPSTER-SHAFER THEORY IN DECISION MAKING AND OPTIMIZATION. Decision Analysis Using Belief Functions (T. Strat). Interval Probabilities Induced by Decision Problems (T. Whalen). DEMPSTER-SHAFER THEORY FOR THE MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS. Using Dempster-Shafer's Belief-Function Theory in Expert Systems (P. Shenoy). Nonmonotonic Reasoning with Belief Structures (R. Yager). Index.
by "Nielsen BookData"