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| 가능성 이론× | 입자 컴퓨팅 (정보 입자화)× | |
|---|---|---|
| 분야 | 소프트 컴퓨팅 | 소프트 컴퓨팅 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1988 | 1997 |
| 창시자≠ | Lotfi Zadeh; Didier Dubois & Henri Prade | Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao |
| 유형≠ | Uncertainty quantification framework | Framework for multi-granularity information processing |
| 원전≠ | Dubois, D., & Prade, H. (1988). Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press. ISBN: 978-0-306-42520-2 | Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111–127. DOI ↗ |
| 별칭 | Fuzzy Possibility Theory, Possibilistic Reasoning, Olasılık Teorisi (Bulanık), Possibility Distribution Theory | information granulation, computing with granules, three-way granular computing, tanecikli hesaplama |
| 관련 | 3 | 3 |
| 요약≠ | Possibility Theory is a mathematical framework for representing and reasoning under uncertainty, introduced by Lotfi Zadeh in 1978 and systematically developed by Didier Dubois and Henri Prade in their 1988 monograph. It uses possibility distributions — functions assigning a degree in [0,1] to each element of a universe — to encode what is plausible or consistent with available information, complementing probability theory for situations where data is scarce or knowledge is imprecise. | Granular computing is a problem-solving paradigm that processes information in 'granules' — clumps of objects drawn together by indistinguishability, similarity, or functionality — rather than at the level of individual data points. Articulated by Lotfi Zadeh in 1997 as fuzzy information granulation and developed into a broad framework, it provides a unifying umbrella over fuzzy sets, rough sets, and interval methods, letting analysis move to whichever level of detail a problem actually requires. |
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