Some General Approximation Error and Convergence Rate Estimates in Statistical Learning Theory
In statistical learning theory, reproducing kernel Hilbert spaces are used basically as the hypothese space in the approximation of the regression function. In this paper, in connection with a basic formula by S. Smale and D. X. Zhou which is fundamental in the approximation error estimates, we shall give a general formula based on the general theory of reproducing kernels combined with linear mappings in the framework of Hilbert spaces. We shall give a prototype example.
Keywordslearning theory convergence rate approximation reproducing kernel Hilbert space linear mapping inequality
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