Probably Approximately Correct learning (PAC learning)
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Probably Approximately Correct (PAC) learning is a foundational framework in computational learning theory that formalizes what it means for an algorithm to efficiently learn a concept from examples with high probability and small error.
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Leslie Valiant
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Leslie Valiant
("“A Theory of the Learnable”")
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poverty of the stimulus argument
("Gold’s theorem in language learnability")
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