Linganisha mbinu

	Unyambulishaji wa Kesi (CBR)	Mti wa Uamuzi	Ramani za Utambuzi wa Fuz (FCM)
Nyanja	Ukokotoaji Laini	Ujifunzaji wa Mashine	Ukokotoaji Laini
Familia	Machine learning	Machine learning	Process / pipeline
Mwaka wa asili	1994	1984	1986
Mwanzilishi	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)	Breiman, Friedman, Olshen & Stone	Bart Kosko
Aina	Experience-based (analogical) problem solving	Recursive partitioning (if-then rules)	Fuzzy causal/feedback network for scenario analysis
Chanzo asilia	Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65–75.
Majina mbadala	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	FCM, Kosko cognitive map, causal cognitive map, bulanık bilişsel haritalar
Zinazohusiana	2	5	4
Muhtasari	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	A fuzzy cognitive map, introduced by Bart Kosko in 1986, represents a system as a network of concepts connected by signed, weighted causal links, and simulates how the concepts influence one another over time. By combining the intuitive structure of a cognitive map with fuzzy weights and iterative activation, FCMs let experts encode causal knowledge and then run what-if scenarios — making them popular for policy analysis, strategic decision-making, and modelling complex socio-technical systems.