A Theory of Cross-Validation Error

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Journal titleJournal of Experimental and Theoretical Artificial Intelligence (JETAI)
Subjectcross validation; curve fitting; AIC; bias; variance; ajustement de courbe; AIC; biais; variance
AbstractThis paper presents a theory of error in cross-validation testing of algorithms for predicting real-valued attributes. The theory justifies the claim that predictingreal-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.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number35072
NPARC number8913080
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Record identifier68cc9d13-ee31-4e00-aa3c-087c3f3471de
Record created2009-04-22
Record modified2016-05-09
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