TY - GEN
A1 - Klęsk, Przemysław
A2 - Korbicz, Józef - red.
A2 - Uciński, Dariusz - red.
PB - Zielona Góra: Uniwersytet Zielonogórski
N2 - 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.
N2 - 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.
L1 - http://zbc.uz.zgora.pl/Content/46871/AMCS_2010_20_3_9.pdf
L2 - http://zbc.uz.zgora.pl/Content/46871
KW - regression estimation
KW - model comparison
KW - complexity selection
KW - cross-validation
KW - generalization
KW - statistical learning theory
KW - generalization bounds
KW - structural risk minimization
T1 - Probabilities of discrepancy between minima of cross-validation, vapnik bounds and true risks
UR - http://zbc.uz.zgora.pl/dlibra/docmetadata?id=46871
ER -