%A Peter D. Turney
%J Journal of Experimental and Theoretical Artificial Intelligence
%T A theory of cross-validation error
%X This 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.
%D 1994
%P 361-391
%L cogprints1820
%V 6

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