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- W3091595014 abstract "This paper is a plea for developing possibilistic learning methods that would be consistent with if-then rule-based reasoning. The paper first recall the possibility theory-based handling of cascading sets of parallel if-then rules. This is illustrated by an example describing a classification problem. It is shown that the approach is both close to a possibilistic logic handling of the problem and can also be put under the form of a max-min-based matrix calculus describing a function underlying a structure somewhat similar to a max-min neural network. The second part of the paper discusses how possibility distributions can be obtained from precise or imprecise statistical data, and then surveys the few existing works on learning in a possibilistic setting. A final discussion emphasizes the interest of handling learning and reasoning in a consistent way." @default.
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- W3091595014 date "2020-01-01" @default.
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- W3091595014 title "From Possibilistic Rule-Based Systems to Machine Learning - A Discussion Paper" @default.
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- W3091595014 doi "https://doi.org/10.1007/978-3-030-58449-8_3" @default.
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