Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!uwm.edu!csd4.csd.uwm.edu!markh From: markh@csd4.csd.uwm.edu (Mark William Hopkins) Newsgroups: comp.ai.neural-nets Subject: Re: Backprop Weight Initialization Message-ID: <8150@uwm.edu> Date: 7 Dec 90 05:27:05 GMT References: <1990Dec6.161422.5314@cs.utk.edu> Sender: news@uwm.edu Organization: University of Wisconsin - Milwaukee Lines: 17 In article bellido@psych.stanford.edu writes: > >What I think is really important is to find some way on wich >backpropagation can be made almost independent of this initial >configuration... I know this may not sound like much, but one sure bet is to initialize the weights close to their final values... :) Other than that, it really sounds like an impossible problem. Think of what would happen if the error function looked like a lunar surface with billions of craters deep and shallow all over the place. Doing backpropagation on it would be like trying to find a real 'deep' crater on the moon by riding a lunar rover constantly downhill from where the lunar lander set down. You really have to be near the crater to find it, else it could be on the other size of the moon...