A Theory of Cross-Validation Error

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dc.creatorTurney, Peter D.
dc.date2002-12-11
dc.date.accessioned2026-07-07T03:19:15Z
dc.date.available2026-07-07T03:19:15Z
dc.descriptionThis paper presents a theory of error in cross-validation testing of algorithms for predicting real-valued attributes. The theory justifies the claim that predicting real-valued attributes requires balancing the conflicting demands of simplicity and accuracy. Furthermore, the theory indicates precisely how these conflicting demands must be balanced, in order to minimize cross-validation error. A general theory is presented, then it is developed in detail for linear regression and instance-based learning.
dc.description48 pages
dc.identifierhttps://arxiv.org/abs/cs/0212029
dc.identifierhttp://arxiv.org/abs/cs/0212029
dc.identifierJournal of Experimental and Theoretical Artificial Intelligence, (1994), 6, 361-391
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31388
dc.subjectMachine Learning
dc.subjectComputer Vision and Pattern Recognition
dc.subjectI.2.6; I.5.2
dc.titleA Theory of Cross-Validation Error
dc.typetext

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