HDTenEightyP/NetNews huggingface dataset:
From: daniel.offutt@mixcom.mixcom.com (daniel.offutt)
Newsgroups: comp.ai.neural-nets
Subject: Re: NNs in chemistry or related fields
Keywords: Aqueous Solubility of Organic Compounds
Date: 15 Dec 92 23:12:51 GMT
Distribution: usa
Organization: Milwaukee Internet Xchange BBS, Milwaukee, WI U.S.A.

Here are a few references on applications of neural nets in chemisty,
biochemistry, etc.: 

Qian & Sejnowski.
Predicting the Secondary Structure of Globular
Proteins Using Neural Network Models.
J. Mol. Biol. (1988) 202, 865-884.

Holley and Karplus.
Protein secondary structure prediction with a neural network.
Proc. Natl. Acad. Sci. USA
Vol. 86, pp 152-156, 1/89.

Whitesides.
What Will Chemistry Do in the Next Twenty Years?
Angew. Chem. Int. Ed. Engl. 29 (1990) 1215-1216.

Elrod and Maggiora.
Applications of Neural Networks in Chemistry.  1. Prediction of Electrophilic
Aromatic Substitution Reactions.
J. Chem. Inf. Comput. Sci. 1990, 30, 477-484.

Scientific American, 4/90, pp24-26.
(Brief article on tertiary structure prediction.)



-- 
Dan Offutt
From: nhr@eng.cam.ac.uk (N.H. Russell   CUED)
Newsgroups: comp.ai.neural-nets
Subject: Looking for report
Date: 15 Dec 92 20:44:26 GMT
Organization: Cambridge University Engineering Department, UK

I am interested in getting a copy of the following report:

`Influence of target functions and context on the spotting
behaviour of RNN phoneme classifiers', R. Couwenburg and
P. Wittenburg, Internal report, Max Planck Institute for
Pyscholinguistics, Wandtlaan, Nijmegen, Netherlands.

Any information which would assist in getting a copy
quickly would be appreciated (eg, Email addresses, FTP availability).

Neil Russell,
Cambridge University Engineering Dept,
UK.
From: tcnijeho@cs.ruu.nl (Tjeerd Nijeholt)
Subject: Waardeloze begeleiding Progr.W1!
Summary: Waar blijven die begeleiders, ze zijn er nooit, zelfs niet oop inlevdag
Date: Mon, 14 Dec 1992 14:32:41 GMT
Organization: Utrecht University, Dept. of Computer Science
Keywords: ergernis

Op de dag dat studenten hun laatste opdracht moeten inleveren is er geen (ik
herhaal, geen enkele) studentassistent om je te begeleiden. Ongelooflijk maar
waar. En dat terwijl sommigen (zoals ik) hier maar een half tot een keer per
week komen. En waar je je opgaven kan ophalen is mij ook nog nooit duidelijk
geworden. Het tentamen W1 is ook (net zoals opgave 2) nog steeds niet negekeken heb ik begrepen (14-12-92)
   HOE IS HET MOGELIJK!!

   NOU JA, HET VALT ALLEMAAL OOK WEL MEE DENK IK, ALS IK DE ANDERE KANT TE
   HOREN KRIJG! GROETJES!
From: pdb@dcs.ed.ac.uk (Paul Bedworth)
Newsgroups: comp.ai.neural-nets
Subject: character recognition
Date: 15 Dec 92 15:51:03 GMT
Reply-To: pdb@dcs.ed.ac.uk (Paul Bedworth)
Organization: Department of Computer Science, University of Edinburgh

We are exprimenting with character recognition using the backprop
neural net alogrithm, but alack, we have few exemplar letters!

We could really do with some help. If you'd like to take part,
all you have to do is mail us with an exemplar letter of your
choice. This should be a capital letter represented in binary,
of size 8x8 bits. Each line should be terminated by a hash.

As an example:

00001000#
00010100#
00100010#
01000001#
01111111#
01000001#
01000001#
01000001#

Go on have a bash! Send the finished letters to "bjoo@castle.ed.ac.uk"
Please don't use banner, that's cheating.

Thanks a lot:

----------------------------------------------------------------------------

  Balint Joo <bjoo@castle.ed.ac.uk> and
  Paul Bedworth <pdb@dcs.ed.ac.uk> and
  Andrew D. Kennedy <adk@dcs.ed.ac.uk> (the one with flames).

-------------------------------------------------------------------------------
"LADY CAPULET	A crutch, a crutch! why call you for a sword?" - W.Shakespeare
-- 

"Chicken is absolutely foul."
From: tfpayn01@ulkyvx.louisville.edu
Subject: Re: Need Assistance on Threat Assessment
Date: Tue, 15 Dec 1992 15:48:42 GMT
Organization: University of Louisville

In article <1992Dec13.194347.22076@cs.ucf.edu>, tas@engr.ucf.edu (Taha Sidani) writes:
> Hi
> 
> 
> I am interested in doing research in the area of "threat assessment" in a 
> simulation that may involve neural nets and AI.  
> 
> In other words, I would like anything that pertains to machine situation 
> awareness.
> 
> Please send me ANY related work, references, systems, and/or general 
> information.
> 
> Thank you very much in advance,
> 
> 
> Please reply to 
> 
> taha@ists.engr.ucf.edu
-- 
	Again, please include me in any information that may be available. 
Thanks for you assistance ahead of time!!


/  / // / / /  / /  / / // / / // / //  / /   /  // /  / / / / // // / / / // /
// // /  / // /// // / / /// /// // /// /// // // /// / ///// // /   / // // /
/  /  ________|\_______________________________________________   /  /  // / /
/ // |\_\_\_\_| |_____________________________________________/   / / / / / / /
// / / /      |/  / / //  // /  / / / //  /  /  / /  /  /  / /  /  / / / /// /
/  / /  /  ///  / /  /  Tom Payne *** Louisville, KY	 / //  /  /  /  / / / /
 /  //TFPAYN01@ULKYVX.LOUISVILLE.EDU   /  / INFTECR1@ULKYVM.LOUISVILLE.EDU /// /
/ // / /  /   / / / /  ///  // (502) 635-2473 / / // // // / /////  / / // // / 
<><><><><> ARE <><><><> YOU <><><><> READY <><><><> TO <><><><> DIE? <><><><><>

Newsgroups: comp.ai.neural-nets
From: krus@cs.concordia.ca (Mike Krus)
Subject: Planet mailing list
Organization: Computer Science, Concordia University, Montreal, Quebec
Date: Thu, 17 Dec 1992 03:04:59 GMT

Hi,

could someone provide me with email address of the Planet Mailing list
administrator??

The account from which I was subscribed on that mailing list was deleted as
the end of term approaches without the system administrator warning anybody!
So I could unsubcribe and mail will probably bounce back god know where!?!!

Thanks,

-- 
Michael Krus.					 Email : krus@cs.concordia.ca
Concordia University				Universite d'Orsay - Paris XI
Montreal, Canada					        Paris, France
Keyboard? How quaint!
Newsgroups: comp.ai.neural-nets
From: a0mali01@orion.spd.louisville.edu (Aleksander Malinowski   Olek)
Subject: Re: Neural net text, finally!
Summary: A new and very interesting book on neural nets is presented.
         Not only for students !!!
Date: Fri, 18 Dec 1992 16:57:40 GMT
Organization: University of Louisville
Keywords: textbook neural networks

It looks like we finally have a book available in the academic discipline of
neural networks. "Introduction to Artificial Neural Systems", by J. M. Zurada,
published recently (1992) by West, explains in detail how learning machines
called neural networks work. Comprehensive, fine, unified, lucid exposition and
numerous examples have provided me with a very good understanding and
appreciation for artificial neural systems. I therefore strongly recommend this
book to serious students and neural network modelers. Other strengths of this
book include great figures, complete algorithms (just to be implemented), and a
diskette with software (source code & program compiled for PC),

To save your time, here are the numbers where you could order the book:
1-800-328-9352 (domestic USA), or
612-687-7302 (foreign and all fax orders).

a0mali01@starbase.spd.louisville.edu
(Computer Science & Engineering Ph.D. student.)



-- 
                       	Olek @ UofL :-).
..........................................................................
     Aleksander Malinowski @ University of Louisville, Kentucky, USA     .
     Ph.D. Student @ Computer Science & Electrical Engineering Dept.     .
  e-mail: a0mali01@starbase.spd.louisville.edu                           .
          2:481/1@fidonet.org & 2:481/2.4@fidonet.org (fwd 2 UUCP :-)    .
  for more informations try: finger a0mali01@starbase.spd.louisville.edu .
..........................................................................
Newsgroups: comp.ai.neural-nets
From: evol@infko.uni-koblenz.de
Subject: Re: Integer neural networks?
Organization: University of Koblenz, Germany
Date: Mon, 14 Dec 1992 12:51:35 GMT

In article <R5BFVB4w165w@west.darkside.com> max@west.darkside.com (Erik Max Francis) writes:
>Anyone know of any good schemes (or books which describe schemes) of 
>neural networks that deal with only integer math?
>
>I'm looking more to use the neural network as a "black box" that receives 
>certain inputs, does manipulations, and then spits out certain outputs.  
>Finite state machines would work fine, too, but there are too many 
>(potential) inputs to implement reasonably.
>

Hallo !

Some weeks ago Donald Tveter posted a backprop implementation to
comp.sources.misc, which can use both integer and float arithmetic. 
If have compiled it for NeXTstation and SPARC2. There is no need to
use integer arithmetic on these machines. Floating point arithmetic
is as fast as integer arithmetic or even faster if you are using a
fast Coprozessor. Nevertheless integer arithmetic is fine for machines
without an arithmetic Coprozessor. If you want a hardware implementation
of your ANN you have to use integer arithmetic in most cases.


Randolf Werner 

Newsgroups: comp.ai.neural-nets
From: liyu@bnr.ca (whyme)
Subject: papers on temporal neural-networks
Organization: Bell-Northern Research, Ontario, Canada
Date: Sun, 13 Dec 1992 05:03:49 GMT

Hi, 

  Could anyone please recommend me some papers/books 
  on Temporal Neural-Networks and their applications
  in the OCR domain? Any help would be greatly appreciated.

  Please e-mail your reply to me.

Thanks very much. 
Newsgroups: comp.ai.neural-nets
From: vsvankay@acs.ucalgary.ca (Vidya Sagar Vankayala)
Subject: When to choose ART/Kohonen Feature Map
Summary: help please
Date: Sun, 13 Dec 92 23:26:08 GMT
Organization: E & C Engg., Univ. Of Calgary, Calgary, Canada
Keywords: ART, Kohonen Feature Map, Differences
Followup-To: comp.ai.neural-nets


Hi,

What are the factors affecting the decision of using ART/Kohonen map
for clustering ?

My understanding is that Kohonen Map can be used when the # of
clusters can be fixed before hand, and there is noise in the input
patterns. 

ART, seems to be used when # of clusters are unspecified [incremental
addition of clusters ?] and the input patterns are free of noise.

Can somebody clarify these points ?

I would appreciate all replies. Please email to
vsvankay@acs.ucalgary.ca.

Thanx veryu much.

Cheers,
Vidya



-- 
Vidya 'Hima' Sagar 
vsvankay@acs.ucalgary.ca (or) vankay@enel.ucalgary.ca
Newsgroups: comp.ai.neural-nets
From: rowe@engin.umich.edu (Steven Rowe)
Subject: Re: Questions about sigmoids etc.
Date: Sun, 13 Dec 92 04:08:37 EST
Organization: University of Michigan Engineering, Ann Arbor
Keywords: Sigmoids, output layers

In article <1992Dec10.084458.12506@dxcern.cern.ch> block@dxlaa.cern.ch (Frank Block) writes:
>
>In article <1992Dec9.160218.25286@cs.brown.edu>, pcm@cs.brown.edu (Peter C. McCluskey) writes:
>|> In article <1992Dec8.161935@sees.bangor.ac.uk>, paulw@sees.bangor.ac.uk 
>|> (Mr P Williams (AD)) writes:
>|> |> 	For backpropagation networks (i.e. Rumelhart ,Mclelland and Williams), 
>|> |> it is neccessary to have a monotonically increasing, DIFFERENTIABLE 
>|> |> function as the output
>|> 
>|>  It is my understanding that differentiability is needed only for
>|> proving that a network does gradient descent. I haven't heard a good
>|> reason for believing that non-differentiable functions are inferior.
>|> 
>|> ----------------------------------------------------------------------
>|>   >> Peter McCluskey >> pcm@cs.brown.edu >>   Reunite Gondwanaland!
>|> ----------------------------------------------------------------------
>|> 
>
>But how are you going to train a network with non-differentiable functions?
>Certainly not with the standard BP?
>
>Frank Block

Here I go, singing the praises of the Alopex algorithm again...
See "Universal Learning Without Gradient Information", K.P. Unnikrishnan
& K.P. Venugopal, submitted to Neural Computation on 2 Dec. 1992.

-- 
Steve Rowe: B0 f- w- c g+ k+ s- e+ r-
I drank too much beer, then tried to sober up by drinking
too much coffee.  The result was that I was still making
irrational decisions, but I was making them *FAST*!  -me
From: lordSnooty@cup.portal.com (Andrew - Palfreyman)
Newsgroups: comp.ai.neural-nets
Subject: Re: NNs that learn to learn
Date: Mon, 14 Dec 92 20:25:51 PST
Organization: The Portal System (TM)
Distribution: world

Learning about learning to learn, or meta-meta learning.
I knew it would come to this.

In case anyone wonders, it's meta's all the way down :-)

Andrew Palfreyman
Newsgroups: comp.ai.neural-nets
From: yu@cs.ucf.edu (Henry Yu)
Subject: Robert A. Jacobs e-mail address
Summary: Robert A. Jacobs e-mail address
Keywords: Jacobs, Delta-Bar-Delta
Organization: University of Central Florida
Distribution: comp.ai.neural-nets
Date: Sat, 12 Dec 1992 02:16:06 GMT


Does anyone know how I can get in touch with Robert A. Jacobs (e-mail address, phone number, etc.)?  He wrote a paper in 1988 article in Neural Networks on
the delta-bar-delta algorithm.

Thanks in advance.

Henry Yu

email:	henry-yu@kmail.ksc.nasa.gov
voice:  (407) 867-4847
Newsgroups: comp.ai.neural-nets
From: waleed@sun14.vlsi.uwaterloo.ca (Fakhr M. Waleed)
Subject: From PNN to MLP
Organization: University of Waterloo
Date: Fri, 18 Dec 1992 21:33:27 GMT

Hi,
I have been trying to map PNN (Gaussian sum networks) to MLP
at least approximately, I have moderate success, any body
knows any references (except the one: Nearest Neighbor to MLP?)

I know that any sigmiod can be seen to implement the class posterior
between two Gaussian classes, any body exploited this idea?

Thanks
Newsgroups: comp.ai.neural-nets
From: u906262@bruny.cc.utas.edu.au (Robert John Martin)
Subject: NNs in chemistry or related fields
Summary: Paramters etc for An NN from CHemical environment
Keywords: Aqueous Solubility of Organic Compounds
Organization: University of Tasmania, Australia.
Date: Mon, 14 Dec 1992 00:21:53 GMT

Greetings
I am intrested in knowing if there is ANYbody who has knowledge of uses,
succesful or otherwise of NNs in the fields of CHemistry, Pharmacy or
biochemistry.

I know of the article:
J.Am.Chem.Soc. 1991, 113, 9480-9483
Neural Network Studies. 1. Estimation of the Aqueous Solubility of Organic Compo
unds, Nicholas Bodor, Alan Harget and Ming-Ju Huang
Contribution from the Center for Drug Discovery, College of Pharmacy, J.H.M 
Helath Center, University of Florida, Gainesville, Florida 32610. Recieved 22-Ap
ril-1991.
Where they have trained a NN and then tested on the prediction of unknown compounds, and compared their results to a previous study of regression analysis.

I am intrested in all aspects of this area, including suggestions of uses for
NNs in such environments.
Many Thanks 
Robert Martin

--
Robert Martin u906262@bruny.cc.utas.edu.au
Born: Hobart Tasmania, Australia 12-APR-1971
The World is round, I think. Let's go and find out.
Tasmania isn't small everyone else is too big.
Newsgroups: comp.ai.neural-nets
From: altenbur@plains (Karl Altenburg)
Subject: Re: Ant behaviour
Date: Fri, 18 Dec 1992 06:01:46 GMT
Organization: North Dakota Higher Education Computing Network
X-Newsreader: Tin 1.1 PL4

If you are interested in studying the neural control of insects through
synthesis you might be interested in the work being done by Randall Beer
at Case Western Reserve University in Cleveland, OH.

--
Karl R Altenburg				altenbur@plains.NoDak.edu
North Dakota State University, Fargo, ND  58105

All things are artificial, for nature is the art of God.  SIR THOMAS BROWNE
From: jdd@duke.cs.duke.edu (John Dengler)
Newsgroups: comp.ai.neural-nets
Subject: self scaling NN's
Date: 17 Dec 92 21:30:14 GMT
Organization: Duke University Computer Science Dept.; Durham, N.C.

I am interested in doing some research related to "self-scaling" neural
network learning; i.e., there is no pre-determined fixed topology for
the net - neurons are added to the net as they are needed (this is
dependent on the problem being solved).  Some examples of this type of
learning are the Cascade Correlation and the Upstart algorithms.  If you
have any information or references relevant to self scaling learning,
please respond via e-mail.  Thanks.


-- 
John Dengler, Computer Science Dept., Duke University, Durham, NC 27706 USA
ARPA:	jdd@cs.duke.edu
CSNET:	jdd@duke
UUCP:	decvax!duke!jdd
Newsgroups: comp.ai.neural-nets
From: sethi@usha.cs.wayne.edu (Ishwar K. Sethi)
Subject: Special Issue - Machine Vision and Applications
Followup-To: sethi@cs.wayne.edu
Keywords: neural nets, machine vision
Organization: Wayne State University, Detroit, MI
Date: Tue, 15 Dec 1992 20:14:59 GMT

Call for Papers
__________________________________________________________________

Special Issue of the Machine Vision and Applications on Neural
Networks for Machine Vision

The developments in the artificial neural network technology in
recent years have provided machine vision researchers and developers
with new tools and techniques to build machine vision algorithms and
systems that exhibit human-like visual perception capabilities.
The goal of the special issue is to capture these developments in
neural network theory and its applications to machine vision and to
provide the readers with an overview of the state-of-the-art. To meet
this goal, papers are solicited for the special issue which is scheduled
to appear in early 1995. Possible topics for the special issue, but not
limited to, include the followings:

*  Learning and Self-Organization for Segmentation, Feature Extraction,
   and Recognition.
*  Motion Detection, Tracking, and Characterization using Neural Networks.
*  Hardware Implementations including Smart Vision Chips.
*  Neural Networks for Multisensory Processing.
*  Automated Visual Monitoring and Inspection using Neural Networks.
*  Reverse Engineering for Machine Vision using Neural Networks.

Papers emphasizing technical details and theoretical background of machine
vision systems having strong neural network components and currently in use
are especially welcome.

The papers should be prepared following the Machine Vision and Applications
guidelines. All papers will be reviewed according to the guidelines of the
Machine Vision and Applications . Please submit four copies of your manuscript
to:

  Professor Ishwar K. Sethi
  Department of Computer Science
  Wayne State University
  Detroit, MI 48202
  U.S.A.

The deadline for submission is July 31, 1993. For enquiries, send E-mail to
sethi@cs.wayne.edu or fax to 313-577-6868.
Newsgroups: comp.ai.neural-nets
From: krish@gfx.engga.uwo.ca (Mr. Venu Krishnapura)
Subject: Need help with BP netsHi Everyone
Organization: Univ. Western Ontario, London Ont. CA
Date: Tue, 15 Dec 1992 15:36:30 GMT

Hi everyone:
   I seem to have messed up my earlier posting.  I am reposting
it here.

	I am trying to model a PRBS type data using BP nets.
I am using a 2 layer network with the inner layer having a 
sigmoidal transfer function and the outer layer having a linear 
transfer function.  My problem is that the network instead of 
following the PRBS data converges to the mean of the PRBS band
width.
	My questions are:
      i)  Am I justified in using the above transfer functions? 
If not, what should I be using?
     ii)  Why is the network behaving so?

	I would appreciate your comments and suggestions.  You 
may mail the same to my email address given below.

Thanks 
venu
-----------------------------------------------------------------
Venugopal G. Krishnapura             (519) 679 2111 Ext:8308 
Internet: krish@gfx.engga.uwo.ca   
Newsgroups: comp.ai.neural-nets
From: elelko@nuscc.nus.sg (Kwok-Onn Looi)
Subject: Re: FAQ
Organization: National University of Singapore
X-Newsreader: Tin 1.1 PL4
Date: Fri, 18 Dec 1992 08:09:37 GMT


i have looked at news.answer but can't find the faq for this group. is
it normally stored in an ftp site somewhere ?

thanks.

rgds,
k.o.looi
From: arms@cs.UAlberta.CA (Bill Armstrong)
Newsgroups: comp.ai.neural-nets
Subject: Re: Questions about sigmoids etc.
Keywords: Sigmoids, output layers
Date: 12 Dec 92 00:35:05 GMT
Organization: University of Alberta, Edmonton, Canada

hm@cs.brown.edu (Harry Mamaysky) writes:

>In article <1992Dec10.084458.12506@dxcern.cern.ch>, block@dxlaa.cern.ch (Frank Block) writes:
>|> 
>|> In article <1992Dec9.160218.25286@cs.brown.edu>, pcm@cs.brown.edu (Peter C. McCluskey) writes:
>|> |> In article <1992Dec8.161935@sees.bangor.ac.uk>, paulw@sees.bangor.ac.uk 
>|> |> (Mr P Williams (AD)) writes:
>|> |> |> 	For backpropagation networks (i.e. Rumelhart ,Mclelland and Williams), 
>|> |> |> it is neccessary to have a monotonically increasing, DIFFERENTIABLE 
>|> |> |> function as the output

>|> 
>|> But how are you going to train a network with non-differentiable functions?
>|> Certainly not with the standard BP?

A partial derivative is only one way of measuring the influence of a
weight change on the output error of a network.  You can also use
ratios of non-infinitesimal perturbations of values too.  Even though
we speak of the derivative we can never take the limit anyway in
implementations.

The ALN adaptive algorithm (atree release 2.7 in pub/atre27.exe for
Windows 3.x on menaik.cs.ualberta.ca [129.128.4.241]) deals with
finite changes. Actually since we are then in a boolean tree, either
the output changes when a "weight" changes, or it doesn't.  The
logical measure is much faster to evaluate in combinational hardware
than the derivative, and it can be done by lazy evaluation of the
logic in software.

I think in large adaptive systems, we have to give up on
differentiability, and even on the idea of only having functions.  I
think we need to learn relationships that are many to many.  In the
general case, I don't see any way we can use derivatives, however
finite perturbations can still be used to reduce error.  This error
reduction combined with Widrow's idea of "least disturbance" provides
lots of possible adaptive algorithms.

--
***************************************************
Prof. William W. Armstrong, Computing Science Dept.
University of Alberta; Edmonton, Alberta, Canada T6G 2H1
arms@cs.ualberta.ca Tel(403)492 2374 FAX 492 1071
Newsgroups: comp.ai.neural-nets
From: lombardo@ghost.dsi.unimi.it (franco lombardo)
Subject: Neurons on [0,1]
Organization: Computer Science Dep. - Milan University
Date: Thu, 17 Dec 1992 11:36:22 GMT

Hi,
     I'm trying  to  theach to a net with one hidden layer a
function from [0,1] to [0,1], i. e. a function taking its values
in the whole interval [0,1], using back-prop.
Which kind of function colud I use instead of the classical
sigmoidal function?
Colud you tell me some references about this?

Thanks in advance!
		  
		   Franco Lombardo.

SORRY FOR MY BAD ENGLISH!


*******************************************************************************
Franco Lombardo                  |    Dipartimento di Scienze dell'Informazione,
lombardo@ghost.dsi.unimi.it      |    Universita' degli Studi di Milano, Italy.  
*******************************************************************************

Newsgroups: comp.ai.neural-nets