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| 자료유형 | 단행본 |
| 개인저자 | Yager Ronald R1941-.,
Kacprzyk Janusz. , Fedrizzi Mario1949-., |
| 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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모바일발송
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 분관대출 | 서비스 |
|---|---|---|---|---|---|---|---|---|
| 1 | 중앙도서관/서고6층/ | 006.3 A2444 | 111297202 | 대출가능 |
|
목차/초록
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
도서관정보
