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Abstract: crossvalidation [2], and also traditional cross-validation. These methods often work well in practice, but the theoretical mechanism for small sample cases is not still clear. For the second approach, the asymptotic approximation is used for estimating the expectation of JG over training input points {xm} M m=1 . This approach includes Akaike's information criterion (AIC) [1] and its various derivatives. Although it is theoretically shown that AIC gives an asymptotic unbiased estimate of the ... (Update)
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BibTeX entry: (Update)
@misc{ sugiyama-subspace,
author = "Masashi Sugiyama and Hidemitsu Ogawa",
title = "Subspace Information Criterion - Unbiased Generalization Error Estimator
for Linear Regression",
url = "citeseer.ist.psu.edu/article/sugiyama00subspace.html" }
Citations (may not include all citations):1291 The nature of statistical learning theory (context) - Vapnik - 1995
594 A new look at the statistical model identification (context) - Akaike - 1974
107 Some comments on CP (context) - Mallows - 1973
85 Network information criterion--- Determining the number of h.. - Murata, Yoshizawa et al. - 1994
70 Smoothing noisy data with spline functions: Estimating the c.. (context) - Craven, Wahba - 1979
22 Subspace information criterion for model selection - Sugiyama, Ogawa - 2000
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