Machine learningUncertainty theory
软集理论
软集理论是一种通过参数化集合族来处理不确定性和模糊性的数学框架。它由Dmitriy Molodtsov于1999年提出，通过将选定参数集中的每个参数映射到该宇宙的某个清晰子集，从而对宇宙中的对象进行近似描述。与概率论或模糊集不同，软集不需要隶属函数或概率分布，这使得该框架在现有不确定性工具因数据不足而失效时，能够避免其不足之处。
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来源
- Molodtsov, D. (1999). Soft set theory—first results. Computers & Mathematics with Applications, 37(4–5), 19–31. DOI: 10.1016/S0898-1221(99)00056-5 ↗
如何引用本页
ScholarGate. (2026, June 2). Soft Set Theory. ScholarGate. https://scholargate.app/zh/soft-computing/soft-set-theory
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