Path: utzoo!utgpu!news-server.csri.toronto.edu!rpi!usc!cs.utexas.edu!helios!tamsun.tamu.edu!jdm5548 From: jdm5548@tamsun.tamu.edu (James Darrell McCauley) Newsgroups: comp.ai.neural-nets Subject: Re: Backprop with additional noisy inputfeature Message-ID: <16382@helios.TAMU.EDU> Date: 21 May 91 06:18:19 GMT References: <3559@wn1.sci.kun.nl> <1991May14.182932.21193@convex.com> <15347@arctic.nprdc.navy.mil> Sender: usenet@helios.TAMU.EDU Followup-To: poster Organization: Ag Engineering Dept, Texas A&M University Lines: 17 In article , arms@cs.UAlberta.CA (Bill Armstrong) writes: [stuff deleted] |> The above all generate new, better training sets. In the case of using |> KNN, the work of creating a good decision boundary is thus removed from |> the network, which just has to learn the training data well. forgive my ignorance (maybe this is a stupid question), but if I'm willing to sacrifice the ability to generalize, what methods are available to pre-process training data to bring similar inputs closer together. I've tried to use bp for what I thought would be a simple classification, but it seems that often the data was "too noisy" and I struggled with convergence. (I'm expecting the end-use data to be less noisy/more consistent) -- James Darrell McCauley, Grad Res Asst, Spatial Analysis Lab Dept of Ag Engr, Texas A&M Univ, College Station, TX 77843-2117, USA (jdm5548@diamond.tamu.edu, jdm5548@tamagen.bitnet)