We prove a general bound on the expected L 2 error of adaptive least squares estimates. By applying it to multilayer feedforward neural network regression function estimates, we are able to obtain fast rates of convergence in special classes of regression functions such as additive functions.
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Köhler et al. (2005) studied this question.
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