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Abstract: Recently, a new model selection criterion called the subspace information criterion (SIC) was proposed. SIC gives an unbiased estimate of the generalization error with finite samples. In this paper, we theoretically and experimentally evaluate the e#ectiveness of SIC in comparison with existing model selection techniques. Theoretical evaluation includes the comparison of the generalization measure, approximation method, and restriction on model candidates and learning methods. The simulations... (Update)
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BibTeX entry: (Update)
@misc{ hidemitsu-model,
author = "Masashi Sugiyama Hidemitsu",
title = "Model Selection with Small Samples",
url = "citeseer.ist.psu.edu/454025.html" }
Citations (may not include all citations):1291 The Nature of Statistical Learning Theory (context) - Vapnik - 1995
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475 Estimating the dimension of a model (context) - Schwarz - 1978
107 Some comments on CP (context) - Mallows - 1973
85 Network information criterion---determining the number of hi.. - Murata, Yoshizawa et al. - 1994
22 Subspace information criterion for model selection - Sugiyama, Ogawa - 2001
19 Model complexity control for regression using VC generalizat.. (context) - Cherkassky, Shao et al. - 1999
17 Further analysis of the data by Akaike's information criteri.. (context) - Sugiura - 1978
5 Generalized information criterion in model selection (context) - Konishi, Kitagawa - 1996
3 the selection of statistical models by AIC (context) - Takeuchi - 1983
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