@misc{cogprints1820, volume = {6}, title = {A theory of cross-validation error}, author = {Peter D. Turney}, year = {1994}, pages = {361--391}, journal = {Journal of Experimental and Theoretical Artificial Intelligence}, url = {http://cogprints.org/1820/}, abstract = {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.} }