Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Risinājums, kas balstīts uz gadījumiem (CBR)× | Fuzzy cognitive maps (FCM)× | |
|---|---|---|
| Nozare | Mīkstā skaitļošana | Mīkstā skaitļošana |
| Saime≠ | Machine learning | Process / pipeline |
| Izcelsmes gads≠ | 1994 | 1986 |
| Autors≠ | Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle) | Bart Kosko |
| Tips≠ | Experience-based (analogical) problem solving | Fuzzy causal/feedback network for scenario analysis |
| Pirmavots≠ | Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59. DOI ↗ | Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65–75. DOI ↗ |
| Citi nosaukumi | CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme | FCM, Kosko cognitive map, causal cognitive map, bulanık bilişsel haritalar |
| Saistītās≠ | 2 | 4 |
| Kopsavilkums≠ | 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 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. |
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