Probabilistic Inductive Inference:a Survey

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dc.creatorAmbainis, Andris
dc.date1999-02-15
dc.date.accessioned2026-07-07T03:23:59Z
dc.date.available2026-07-07T03:23:59Z
dc.descriptionInductive inference is a recursion-theoretic theory of learning, first developed by E. M. Gold (1967). This paper surveys developments in probabilistic inductive inference. We mainly focus on finite inference of recursive functions, since this simple paradigm has produced the most interesting (and most complex) results.
dc.description16 pages, to appear in Theoretical Computer Science
dc.identifierhttps://arxiv.org/abs/cs/9902026
dc.identifierhttp://arxiv.org/abs/cs/9902026
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/33157
dc.subjectMachine Learning
dc.subjectComputational Complexity
dc.subjectLogic in Computer Science
dc.subjectLogic
dc.subjectF.1.1., F.4.1., I.2.3., I.2.6
dc.titleProbabilistic Inductive Inference:a Survey
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