Use of weighting functions for focusing of learning in artificial neural networks

We present a modification to the standard backpropagation algorithm to enhance local fitting. The modification involves weighting the error more heavily for particular regions of the training set. We show that during training our technique gives closer convergence on the region of interest, while retaining the overall global features of the arbitrary function it trained upon.