title: A theory of cross-validation error
creator: Turney, Peter D.
subject: Artificial Intelligence
subject: Machine Learning
subject: Statistical Models
description: 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.
date: 1994
type: Journal (Paginated)
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/1820/3/NRC-35072.pdf
identifier:   Turney, Peter D.  (1994) A theory of cross-validation error.  [Journal (Paginated)]     
relation: http://cogprints.org/1820/