http://cogprints.org/1820/
A theory of cross-validation error
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.
Turney, Peter D.
Artificial Intelligence
Machine Learning
Statistical Models
Peter D.
Turney