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On optimal data split for generalization estimation and model selection

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2 Author(s)
J. Larsen ; Dept. of Math. Modeling, Tech. Univ. Denmark, Lyngby, Denmark ; C. Goutte

The paper is concerned with studying the very different behavior of the two data splits using hold-out cross-validation, K-fold cross-validation and randomized permutation cross-validation. First we describe the theoretical basics of various cross-validation techniques with the purpose of reliably estimating the generalization error and optimizing the model structure. The paper deals with the simple problem of estimating a single location parameter. This problem is tractable as non-asymptotic theoretical analysis is possible, whereas mainly asymptotic analysis and simulation studies are viable for the more complex AR-models and neural networks

Published in:

Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop.

Date of Conference:

Aug 1999