Case-based learning in a bipolar possibilistic framework

DOI10.1002/int.20309zbMath1155.68072OpenAlexW4242214927MaRDI QIDQ3537545

Eyke Hüllermeier, Jürgen Beringer

Publication date: 7 November 2008

Published in: International Journal of Intelligent Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/int.20309

zbMATH Keywords

predictionpossibility theory

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

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A disaster-severity assessment DSS comparative analysis ⋮ Naive possibilistic classifiers for imprecise or uncertain numerical data

Cites Work

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• Applications of type-2 fuzzy logic systems to forecasting of time-series
• Knowledge-driven versus data-driven logics
• An experiment in linguistic synthesis with a fuzzy logic controller
• On antonym and negate in fuzzy logic

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