Newsgroups: comp.ai.neural-nets Path: utzoo!utgpu!news-server.csri.toronto.edu!rpi!cheng From: cheng@ral.rpi.edu (Wei-Ying Cheng) Subject: quastion Message-ID: Sender: cheng@ral.rpi.edu Nntp-Posting-Host: mars.ral.rpi.edu Organization: Rensselaer Polytechnic Institute, Troy NY Distribution: comp.ai.neural-nets Date: 20 Mar 91 03:55:31 GMT Lines: 15 I have a question which may be interesting: Suppose we have a sample set S which contains a huge number of samples. It is impossible to sum all the samples in the set S. If we choose a sample s=(x,y) from the sample set S randomly according to a prior distribution. We hope NN to learn this sample. Suppose the output of NN is y' if the input of NN is x, then we have a norm d =|y - y'|. It is obvious that d is a random variable. The problem is how to develop a learning rule such that d can be minimized in probablity sence. e.g E(d) is minimized, or P(min(d)) converge to 1, etc. Is there any reference talking about this problem? Since this problem is concerned with the generalization problem of NN, I think it is very interesting. I greatly appreciate any help.