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Advances in the Dempster-Shafer theory of evidence

Advances in the Dempster-Shafer theory of evidence (1회 대출)

자료유형
단행본
개인저자
Yager, Ronald R, 1941-. Kacprzyk, Janusz. Fedrizzi, Mario, 1949-.
서명 / 저자사항
Advances in the Dempster-Shafer theory of evidence / edited by Ronald R. Yager, Janusz Kacprzyk, Mario Fedrizzi.
발행사항
New York :   Wiley,   c1994.  
형태사항
vii, 597 p. : ill. ; 25 cm.
ISBN
0471552488
서지주기
Includes bibliographical references and index.
일반주제명
Neural networks (Computer science) Fuzzy systems. Artificial intelligence. Dempster-Shafer theory.
비통제주제어
Artificial intelligence ,,
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008 041014s1994 nyua 001 0 eng
010 ▼a 92026536
020 ▼a 0471552488
040 ▼a DLC ▼c DLC ▼d UKM ▼d 211009
050 0 0 ▼a QA76.87 ▼b .A39 1994
082 0 0 ▼a 006.3 ▼2 21
090 ▼a 006.3 ▼b A2444
245 0 0 ▼a Advances in the Dempster-Shafer theory of evidence / ▼c edited by Ronald R. Yager, Janusz Kacprzyk, Mario Fedrizzi.
260 ▼a New York : ▼b Wiley, ▼c c1994.
300 ▼a vii, 597 p. : ▼b ill. ; ▼c 25 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Neural networks (Computer science)
650 0 ▼a Fuzzy systems.
650 0 ▼a Artificial intelligence.
650 0 ▼a Dempster-Shafer theory.
653 0 ▼a Artificial intelligence
700 1 ▼a Yager, Ronald R, ▼d 1941-.
700 1 ▼a Kacprzyk, Janusz.
700 1 ▼a Fedrizzi, Mario, ▼d 1949-.

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 006.3 A2444 등록번호 111297202 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차


CONTENTS
FOREWORD = 1
Ⅰ. DEMPSTER-SHAFER THEORY OF EVIDENCE; GENERAL ISSUES
 1. What is Dempster-Shafer's model? = 5
 2. Measures of uncertainty in the Dempster-Shafer theory of evidence = 35
 3. Representation, independence, and combination of evidence in the Dempster-Shafer theory = 51
 4. Focusing versus updating in belief function theory = 71
 5. Combination of compatible belief functions and relation of specificity = 97
 6. Comparative beliefs = 115
 7. Calculus with linguistic probabilities and beliefs = 133
 8. Steps toward efficient implementation of Dempster-Shafer theory = 153
 9. Monte-Carlo methods make Dempster-Shafer formalism feasible = 175
 10. From rough set theory to evidence theory = 193
Ⅱ. FUZZIFICATION OF DEMPSTER-SHAFER THEORY OF EVIDENCE
 11. Mass distributions on L-fuzzy sets and families of frames of discernment = 239
 12. Rough membership functions = 251
Ⅲ. DEMPSTER-SHAFER THEORY IN DECISION MAKING AND OPTIMIZATION
 13. Decision analysis using belief functions = 275
 14. On decision making using belief functions = 311
 15. Dynamic decision making with belief functions = 331
 16. Interval probabilities induced by decision problems = 353
 17. Constraint propagation over a restricted space of configurations, and its use in optimization = 375
Ⅳ. DEMPSTER-SHAFER THEORY FOR THE MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS
 18. Using Dempster-Shafer's belief-function theory in expert systems = 395
 19. Issues of knowledge representation in Dempster-Shafer's theory = 415
 20. Representing heuristic knowledge and propagation beliefs in the Dempster-Shafer theory of evidence = 441
 21. epresentation of evidence by hints = 473
 22. Evidential reasoning with conditional events = 493
 23. A calculus for mass assignments in evidential reasoning = 513
 24. Nonmonotonic reasoning with belief structures = 533
 25. How far are we from the complete knowledge? Complexity of knowledge acquisition in the Dempster-Shafer approach = 555
 26. Mass assignments and fuzzy sets for fuzzy databases = 577
INDEX = 595


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