Path: utzoo!news-server.csri.toronto.edu!rutgers!cs.utexas.edu!helios!diamond!jdm5548
From: jdm5548@diamond.tamu.edu (James Darrell McCauley)
Newsgroups: comp.ai.neural-nets
Subject: Re: Anybody's Experience with Fahlman's Quickprop? (was Re: Are Conjugate Gradient algorithms any good?)
Keywords: Conjugate Gradient algorithms, Back-propagation
Message-ID: <13281@helios.TAMU.EDU>
Date: 11 Mar 91 22:44:39 GMT
References: <47034@nigel.ee.udel.edu> <92992@lll-winken.LLNL.GOV>
Sender: usenet@helios.TAMU.EDU
Reply-To: jdm5548@diamond.tamu.edu (James Darrell McCauley)
Followup-To: comp.ai.neural-nets
Organization: Spatial Analysis Lab, Dept of Ag Engr, TAMU
Lines: 23

In article <92992@lll-winken.LLNL.GOV>, loren@tristan.llnl.gov (Loren Petrich) writes:
[stuff deleted]
|> 
|> 	Has anyone else had experience with Quickprop, and how does it
|> compare with Conjugate Gradients and other such methods?
|> 

I believe that Timothy R. Thomas and Tony L. Brewster at Los Alamos
National Laboratory did a comparison unvolving Quickprop. I just
read a paper by these men called "Experiements in Finding Neural Network
Weights"    [I have this on microfiche, so I can't readily check my facts.
             If you would like to find this paper, the fiche is labeled
             "Office of Scientific and Technical Information, DOE, USA" -
             Ref # LA-11772-MS, E 1.99, DE90-007696 ]

While I'm on the subject, does anyone have an e-mail address for these
authors? In their comparison, they used an 18-component real-valued vector
representing a spectrum of speech.  I'm interested in what those 18 components
were.
-- 
James Darrell McCauley (jdm5548@diamond.tamu.edu, jdm5548@tamagen.bitnet)
Spatial Analysis Lab, Department of Agricultural Engineering,
Texas A&M University, College Station, Texas 77843-2117, USA