Salīdzināt metodes

	Lēmumi trīs daļās	Risinājums, kas balstīts uz gadījumiem (CBR)
Nozare	Mīkstā skaitļošana	Mīkstā skaitļošana
Saime	Machine learning	Machine learning
Izcelsmes gads	2010	1994
Autors	Yiyu Yao	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)
Tips	Decision-theoretic classification framework	Experience-based (analogical) problem solving
Pirmavots	Yao, Y. (2010). Three-way decisions with probabilistic rough sets. Information Sciences, 180(3), 341–353.	Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.
Citi nosaukumi	3WD, Trisecting-and-Acting, Tri-partition Decision Making, Üç Yönlü Kararlar	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme
Saistītās	2	2
Kopsavilkums	Three-Way Decisions (3WD) is a decision-theoretic framework, introduced by Yiyu Yao in 2010, that partitions the universe of objects into three regions—positive (accept), negative (reject), and boundary (abstain)—using probabilistic rough set theory. Unlike binary classifiers that force every object into one of two classes, 3WD explicitly acknowledges uncertainty by allowing a third option: deferring judgment when available evidence is insufficient for a confident decision.	Case-based reasoning solves a new problem by retrieving similar problems solved in the past and adapting their solutions, rather than reasoning from first principles or a trained statistical model. Formalized as the Retrieve-Reuse-Revise-Retain cycle by Aamodt and Plaza in 1994 and popularized by Janet Kolodner, CBR mirrors how human experts in medicine, law, and engineering reason by analogy from remembered cases, and it learns simply by storing each newly solved case.