Rates of convergence for adaptive regression estimates with multiple hidden layer feedforward neural networks

We present a general bound on the expected L₂ error of adaptive least squares estimates. By applying it to multiple hidden layer feedforward neural network regression function estimates we are able to obtain optimal (up to log factor) rates of convergence for Lipschitz classes and fast rates of convergence for some classes of regression functions such as additive functions