Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

October 1, 1996, Vol. 8, No. 7, Pages 1421-1426
(doi: 10.1162/neco.1996.8.7.1421)
© 1996 Massachusetts Institute of Technology
No Free Lunch for Cross-Validation
Article PDF (285.25 KB)
Abstract

It is known theoretically that an algorithm cannot be good for an arbitrary prior. We show that in practical terms this also applies to the technique of “cross-validation,” which has been widely regarded as defying this general rule. Numerical examples are analyzed in detail. Their implications to researches on learning algorithms are discussed.