Newsgroups: comp.ai.neural-nets Path: utzoo!utgpu!news-server.csri.toronto.edu!rpi!omlinc From: omlinc@cs.rpi.edu (Christian Omlin) Subject: fault-tolerance of feedforward networks` Message-ID: Keywords: fedforward networks, sensitivity, weight perturbation Sender: omlinc@cs.rpi.edu Nntp-Posting-Host: cs.rpi.edu Organization: Rensselaer Computer Science, Troy NY Distribution: usa Date: 26 Apr 91 13:56:29 GMT Lines: 31 Hi ! I am running simulations with backprop networks. The network is used as a classifier. I am interested in the sensitivity of the network to perturbations in the weights. My experiments indicate that the performance degrades more rapidly when the weights from the input to the hidden layer are perturbed as opposed to perturbation of weights from the hidden to the output layer. This implies that, for my experiments, the shape of the decision regions is largely determined by the first hidden layer. Are there any references (simulations, etc) confirming this behavior ? Thanks. Christian ---------------------------------------------------------------------------- Christian W. Omlin office: home: Computer Science Department Foxberry Farm Amos Eaton 119 Box 332, Route #3 Rensselaer Polytechnic Institute Averill Park, NY 12018 Troy, NY 12180 USA (518) 766-5790 (518) 276-2930 e-mail: omlinc@turing.cs.rpi.edu ----------------------------------------------------------------------------