@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.}
}

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