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Improving Precision of the Subspace Information Criterion
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2003/07/01
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks and Bioengineering
machine learning, generalization capability, subspace information criterion, generalized inverse, unbiased estimator, variance,
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Evaluating the generalization performance of learning machines without using additional test samples is one of the most important issues in the machine learning community. The subspace information criterion (SIC) is one of the methods for this purpose, which is shown to be an unbiased estimator of the generalization error with finite samples. Although the mean of SIC agrees with the true generalization error even in small sample cases, the scatter of SIC can be large under some severe conditions. In this paper, we therefore investigate the causes of degrading the precision of SIC, and discuss how its precision could be improved.