The cross validation problem

Authors:
John Langford
Affiliations:
TTI-Chicago
Venue:
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Year:
2005

Citing 4
Cited 0

Beating the hold-out: bounds for K-fold and progressive cross-validation

COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Algorithmic stability and sanity-check bounds for leave-one-out cross-validation

Neural Computation
No Unbiased Estimator of the Variance of K-Fold Cross-Validation

The Journal of Machine Learning Research
Generalization error bounds using unlabeled data

COLT'05 Proceedings of the 18th annual conference on Learning Theory

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Abstract

K-fold cross validation is a commonly used technique which takes a set of m examples and partitions them into K equal-size sets (folds) of size m/K. For each set, a classifier is trained on the other sets.