Computational learning theory: Difference between revisions

Revision as of 23:34, 24 November 2022

Revision as of 23:39, 24 November 2022

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* Exact learning, proposed by [[Dana Angluin]];

* [[Probably approximately correct learning]] (PAC learning), proposed by [[Leslie Valiant]]<ref>{{cite journal |last1=Valiant |first1=Leslie |title=A Theory of the Learnable |journal=Communications of the ACM |date=1984 |volume=27 |issue=11 |pages=1134-1142 |url=https://www.montefiore.ulg.ac.be/~geurts/Cours/AML/Readings/Valiant.pdf |ref=ValTotL}}</ref>;

* [[VC theory]], proposed by [[Vladimir Vapnik]] and [[Alexey Chervonenkis]]<ref>{{cite journal |last1=Vapnik |first1=V. |last2=Chervonenkis |first2=A. |title=On the uniform convergence of relative frequencies of events to their probabilities |journal=Theory of Probability and Its Applications |date=1971 |volume=16 |issue=2 |pages=264-280 |url=https://courses.engr.illinois.edu/ece544na/fa2014/vapnik71.pdf |ref=VCdim}}</ref>;

* [[VC theory]], proposed by [[Vladimir Vapnik]] and [[Alexey Chervonenkis]];

* [[Bayesian inference]];

* [[Algorithmic learning theory]], from the work of [[E. Mark Gold]];