ε-Insensitive Modification of Subspace Information Criterion
Xuejun Zhou · 2009
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. In this paper, we give ε-insensitive modification of the subspace information criterion (mSIC), it can improve the precision of SIC.