Title: Optimal Cross-Validation Split Ratio: Experimental
Investigation
Author:
Cyril Goutte and
Jan Larsen
Department of Mathematical Modeling - Building 321
Technical University of Denmark, DK-2800 Lyngby, Denmark
Phone: +45 4525 3921,3923
Fax: +45 4587 2599
E-mail: cg,jl@imm.dtu.dk
http://eivind.imm.dtu.dk
Abstract
Cross-validation is a common method for assessing the
generalisation ability of a model in order to tune a regularisation
parameter or other hyper-parameters of a learning process. The use of
cross-validation requires to set yet an additional parameter, the
split rati. While a few texts have investigated
theoretically the asymptotic setting of this ratio, no consensus has
emerged. In this contribution, we investigate the sensitivity and
optimal setting of the split ratio on a particular model, a
non-parametric kernel estimator with adaptive metric.
Preprint, appears in L. Niklasson, M. Bodén and T. Ziemke (eds),
Proceedings of the 8th International Conference on Artificial Neural
Networks (ICANN'98, Skovde), Perspectives in Neural Computing,
Berlin:Springer Verlag, vol. 2, pp. 681-686.
Download:
abstract,
Compressed
Postscript.