Some Results in Statistical Learning Theory with Relevance to Nonlinear System Identification
نویسنده
چکیده
Statistical Learning Theory comprises a collection of techniques that have been developed in order to theoretically analyse the performance of neural network and other “learning” algorithms. In this paper, a number of recent results in statistical learning theory are summarised in the context of nonlinear system identification. A top-down approach to the problem is taken, leading to the statement of a number of characterisation results. Specific topics covered include empirical risk minimisation, various types of Glivenko-Cantelli classes, scale-sensitive dimensions, sample complexity and the application to dynamic system identification.
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