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PAC learning is a computational learning theory framework proposed by Valiant in 1984.
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11/3/2026
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UnverifiedThe PAC learning framework was proposed by computer scientist Leslie Valiant in 1984, for which he received the 2010 Turing Award.75% similarUnverifiedPAC learning (Probably Approximately Correct learning) is a classic framework in computational learning theory, used to characterize the conditions under which a learner obtains an approximately correct hypothesis with high probability given a finite number of samples.72% similarUnverifiedThe core idea of the Mixture of Experts (MoE) architecture originates from the divide-and-conquer learning theory of 1991.66% similarUnverified布卢姆于20世纪70年代提出掌握学习理论，讨论了结果性评价的局限性65% similarUnverifiedThe independent and identically distributed (i.i.d.) assumption is the cornerstone of classical statistical learning theory and is a prerequisite for the PAC learning framework and VC dimension theory65% similar
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