Functional Analytic Approach to Model Selection — Subspace Information Criterion
نویسندگان
چکیده
The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. In this paper, we propose a new criterion for model selection called the subspace information criterion (SIC). Computer simulations show that SIC works well even when the number of training examples is small.
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