Abstract
Model selection is one of the most important tasks in the identification of black-box systems. In this paper, we give a novel model selection method from the viewpoint of functional analysis. We formulate the system identification problem as a function approximation problem in a reproducing kernel Hilbert space (RKHS), where the approximation error is measured by the RKHS norm. Within this framework, we derive an estimator of the approximation error called the subspace information criterion (SIC) and show its properties.
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