Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!helios!tamsun.tamu.edu!jdm5548
From: jdm5548@tamsun.tamu.edu (James Darrell McCauley)
Newsgroups: comp.ai.neural-nets
Subject: Re: Extracting Rules from a Trained Network
Message-ID: <14940@helios.TAMU.EDU>
Date: 19 Apr 91 00:44:25 GMT
References: <886@uqcspe.cs.uq.oz.au>
Sender: usenet@helios.TAMU.EDU
Followup-To: comp.ai.neural-nets
Organization: Spatial Analysis Lab, Dept of Ag Engr, TAMU
Lines: 22

In article <886@uqcspe.cs.uq.oz.au>, bakker@cs.uq.oz.au (Paultje Bakker) writes:
|> I am interested in pointers to any articles, researchers, papers,
|> or books that have investigated the extraction of rules from a
|> successfully trained neural network.
|> 

On a similar note, Martin Wildberger of General Physics presented something
at the Simulation Multiconference in New Orleans a couple of weeks ago on 
using weights to determine significance of inputs and then "fuzzyfy" them 
and change them to verbage so that a non-techie could understand why a NN 
came to a particular solution.

This is a second-hand account of his talk - I was unable to stay in town.
Does anyone have any references to this type of thing?  This again is
second-hand, but I heard that when folks asked for references (or even copies
of his slides) that he referred them to a publication last year, either
in the SMC Proceedings or some NN conference.

-- 
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)