Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!uunet!mcsun!cernvax!cernvax.cern.ch
From: burow@cernvax.cern.ch (burkhard burow)
Newsgroups: comp.ai.neural-nets
Subject: nn software vs. stat. techniques
Message-ID: <3885@cernvax.cern.ch>
Date: 21 Jan 91 08:26:38 GMT
Sender: burow@cernvax.cern.ch
Organization: CERN, European Laboratory for Particle Physics
Lines: 15

What are the performance and 'elegance' differences between neural net sw and
stat. techniques, e.g. discriminant analysis, when used to assign events to one
of 2 or more populations. Seen by the user as a black box, the 2 methods are
identical, a understood training set of events sets up the machinery and the
unknown events follow.

I certainly understand the performance advantage of hardware neural nets, e.g.
the brain, but what's the story when both of the above methods run on 'normal'
computers. 

I'm looking for comments, facts, arguments, beliefs, pointers to literature,
postings, etc. 

thanks               INTERNET:  burow%13313.hepnet@csa3.lbl.gov
burkhard