# Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risks

International Journal of Applied Mathematics and Computer Science (2010)

- Volume: 20, Issue: 3, page 525-544
- ISSN: 1641-876X

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topPrzemysław Klęsk. "Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risks." International Journal of Applied Mathematics and Computer Science 20.3 (2010): 525-544. <http://eudml.org/doc/208005>.

@article{PrzemysławKlęsk2010,

abstract = {Two known approaches to complexity selection are taken under consideration: n-fold cross-validation and structural risk minimization. Obviously, in either approach, a discrepancy between the indicated optimal complexity (indicated as the minimum of a generalization error estimate or a bound) and the genuine minimum of unknown true risks is possible. In the paper, this problem is posed in a novel quantitative way. We state and prove theorems demonstrating how one can calculate pessimistic probabilities of discrepancy between these minima for given for given conditions of an experiment. The probabilities are calculated in terms of all relevant constants: the sample size, the number of cross-validation folds, the capacity of the set of approximating functions and bounds on this set. We report experiments carried out to validate the results.},

author = {Przemysław Klęsk},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {regression estimation; model comparison; complexity selection; cross-validation; generalization; statistical learning theory; generalization bounds; structural risk minimization},

language = {eng},

number = {3},

pages = {525-544},

title = {Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risks},

url = {http://eudml.org/doc/208005},

volume = {20},

year = {2010},

}

TY - JOUR

AU - Przemysław Klęsk

TI - Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risks

JO - International Journal of Applied Mathematics and Computer Science

PY - 2010

VL - 20

IS - 3

SP - 525

EP - 544

AB - Two known approaches to complexity selection are taken under consideration: n-fold cross-validation and structural risk minimization. Obviously, in either approach, a discrepancy between the indicated optimal complexity (indicated as the minimum of a generalization error estimate or a bound) and the genuine minimum of unknown true risks is possible. In the paper, this problem is posed in a novel quantitative way. We state and prove theorems demonstrating how one can calculate pessimistic probabilities of discrepancy between these minima for given for given conditions of an experiment. The probabilities are calculated in terms of all relevant constants: the sample size, the number of cross-validation folds, the capacity of the set of approximating functions and bounds on this set. We report experiments carried out to validate the results.

LA - eng

KW - regression estimation; model comparison; complexity selection; cross-validation; generalization; statistical learning theory; generalization bounds; structural risk minimization

UR - http://eudml.org/doc/208005

ER -

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