2002/12/11 by Peter D. Turney
Computer Science · #cs.LG #cs.CV
published as Journal of Experimental and Theoretical Artificial Intelligence, (1994), 6, 361-391 · 48 pages
arxiv created 2002/12/11 · arxiv updated 2009/11/30
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.