Machine learningCase-based reasoning
基于案例推理 (CBR)
基于案例推理通过检索过去已解决的相似问题并调整其解决方案来解决新问题，而不是从第一性原理或训练过的统计模型进行推理。由 Aamodt 和 Plaza 于 1994 年正式化为检索-重用-修正-保留 (Retrieve-Reuse-Revise-Retain) 循环，并由 Janet Kolodner 推广，CBR 模仿了医学、法律和工程领域人类专家通过类比回忆案例进行推理的方式，并且它通过存储每个新解决的案例来简单地学习。
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来源
- Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59. DOI: 10.3233/AIC-1994-7104 ↗
- Kolodner, J. L. (1992). An introduction to case-based reasoning. Artificial Intelligence Review, 6(1), 3–34. DOI: 10.1007/BF00155578 ↗
如何引用本页
ScholarGate. (2026, June 2). Case-Based Reasoning (CBR). ScholarGate. https://scholargate.app/zh/soft-computing/case-based-reasoning
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