HDTenEightyP/NetNews huggingface dataset:
From: jg10@prism.gatech.EDU (GILMORE,JOHN F)
Newsgroups: comp.ai.neural-nets
Subject: 14th IMACS World Congress - Neural Networks Sessions
Keywords: neural networks
Date: 18 Nov 92 17:01:43 GMT
Organization: Georgia Institute of Technology


     14th IMACS World Congress on Computational and Applied Mathematics
     ******************************************************************

   July 11-15, 1994 - at the Georgia Institute of Technology, Atlanta, GA


The International Association for Mathematics and Computers in Simulation will
have several sessions on neural network at the 14th IMACS World Congress.

Interested authors should submit papers by December 1st to:

                                 W. F. Ames
                          School of Mathematics
                     Georgia Institute of Technology
                      Atlanta, GA  30332-0160  USA

For further information on the conference program, please call (404) 853-9112.-- 
 -------------------------------------------------- -----------------------
|                 John F. Gilmore                  |   INTERNET Address    |
|   GTRI-CSITL, Georgia Tech, Atlanta, GA  30332   | jg10@prism.gatech.edu |
 -------------------------------------------------- -----------------------
From: bond@barracuda.cps.msu.edu (Curtis R Bond)
Newsgroups: comp.ai.neural-nets
Subject: ftp sites
Date: 21 Nov 92 23:26:45 GMT
Organization: Dept. of Computer Science, Michigan State University

This maybe a FAQ but here it goes. Could someone e-mail me a list of anonymous
ftp sites which have C++ or C code for ANN, and maybe examples of ANN in a
program. 

	Thanks
		Curt Bond

bond@cps.msu.edu
Newsgroups: comp.ai.neural-nets
From: choy@skorpio.usask.ca (I am a terminator.)
Subject: Algorithmic equivalents and performance?
Organization: University of Saskatchewan, Saskatoon, Canada
Date: Fri, 20 Nov 1992 19:34:09 GMT

A neural network is an iterative architecture composed of simple
I/O devices. How would a neural net compare in performance to a
non-neural net implementation of the neural net's "algorithm"?
Some kind of algorithmic (likely parallel) implementation. Is there
a lot of room of optimization?

Henry Choy
choy@cs.usask.ca
Newsgroups: comp.ai.neural-nets
From: vsvankay@acs.ucalgary.ca (Hima Sagar- The Humming Bird)
Subject: Differences between Pattern Recog. and NN
Date: Thu, 19 Nov 92 22:59:50 GMT
Distribution: na
Organization: E & C Engg., Univ. Of Calgary, Calgary, Canada
Keywords: PR, NN, differences

Hi,

Is there any informative paper/book dealing with the differences
between pattern recognition and NNs used as pattern recognizers ?

If some one can explain the differences, I wuld like to hear from
them. Please mail to vsvankay@acs.ucalgary or vankay@enel.ucalgary.ca
I will summaize if there is interest.


Thank you very much for your help.


Cheers,
Vidya.



-- 
Vidya 'Hima' Sagar 
vsvankay@acs.ucalgary.ca (or) vankay@enel.ucalgary.ca
Newsgroups: comp.ai.neural-nets
From: rs69@cunixb.cc.columbia.edu (Rong Shen)
Subject: Re: How to train a lifeless network (of "silicon atoms")?
Reply-To: rs69@cunixb.cc.columbia.edu (Rong Shen)
Organization: Columbia University
Date: Sun, 22 Nov 1992 21:58:22 GMT

In article <1992Nov22.182325.24185@dxcern.cern.ch> block@dxlaa.cern.ch (Frank Block) writes:

			(junk deleted)

>What you normally do during training is to present (taking you example) the
>words 'hello' and 'goodbye' alternatively. You should not train the net first
>just on one and then, when it has learned to recognize it, on the other.
>The training is a statistical process which in the end (let's hope) converges
>to a good set of weights (a compromise which recognizes all patterns in an
>optimal way).

	Thanks, Frank.

	If I feed the words alternately, how would I train the network
to recognize 99,999 words? Would not the 99,999th word erase the 1st
word?

--
rs69@cunixb.cc.columbia.edu
From: gsbg9079@uxa.cso.uiuc.edu (G. Scott Braley)
Newsgroups: comp.ai.neural-nets
Subject: Re: Neural Nets and C++
Date: 16 Nov 92 15:28:53 GMT
Organization: University of Illinois at Urbana

clin@eng.umd.edu (Charles Lin) writes:



>In article <1992Nov14.020053.10113@sbcs.sunysb.edu>, worrick@csws2.ic.sunysb.edu (William Orrick) writes:
>>
>>As a beginner who did buy the book I would add that it was not very
>>useful to me either.  The code in the back of the book bug-ridden and
>>the text provides little detailed information to help with the theory
>>or the running of the programs.
>>

>  One problem that may cause this (other than carelessness) is that there
>is no (as far as I know) standardized C++.    In any case, if this
>is true, it is better to describe the principles, rather than have
>the person just copy code.   

>--
>Charles Lin
>clin@eng.umd.edu

Actually, there were several subroutines left out of the book.  Contact your bookseller and
ask they to contact Wiley, they will send a disk with the corrected code.

			-Scott

-- 
*****************************************************************************
*  F.B.I.B.M.--The most feared merger  *  gsbg9079@uxa.cso.uiuc.edu         *
*****************************************************************************
Newsgroups: comp.ai.neural-nets
From: tlund@news.weeg.uiowa.edu (Thomas Lund)
Subject: Neural nets and Random walks
Organization: University of Iowa, Iowa City, IA, USA
Date: Sun, 22 Nov 1992 02:07:09 GMT

	Does anyone know of neural networks which approximate random walks?
I had in mind something of the lateral excitatory/inhibitory (hopfield like?)
networks where the relative activity of one (or more) nodes takes a random
walk by way of asynchronous updating.  Anyway, any references, or (off the
record) assurances of existence would be appreciated.

Thanks in advance

 /\//\
  //
\//\/  thomas-lund@uiowa.edu
Newsgroups: comp.ai.neural-nets
From: swann@divsun.unige.ch (SWANN Philip)
Subject: Re: Genetic Algorithms
Keywords: GA
Organization: University of Geneva, Switzerland
Date: Thu, 19 Nov 1992 17:17:20 GMT

In article <1992Nov19.140813.18335@newssun.med.miami.edu>, dbrown@newssun.med.miami.edu (Daniel Brown) writes:

> Can somebody please post a short explanation of what 
> exactly GAs are and how they are related to neural nets?

See the article by Holland in Scientific American a couple of
months back (August?).
Newsgroups: comp.ai.neural-nets
From: shan@ee.ic.ac.uk (Shan Parfilt)
Newsgroups: comp.ai.neural-nets
Subject: nn's and nat. lang.
Summary: possibility of new group?
Date: 16 Nov 92 10:26:18 GMT
Organization: Elec. Eng. Dept., Imperial College London

A few people have, in personal communications, expressed an interest
in setting up a newsgroup or mailing list on the subject of 
neural network models of  natural language. If anyone else
reading this newsgroup is also interested, please make yourself
known! I should add that I am not really in a position to
be the moderator for such a group, so if anyone would like to, please
feel free!
S. Parfitt
Newsgroups: comp.ai.neural-nets
From: jonas@beor.informatik.rwth-aachen.de (Josef Nelissen)
Subject: Re: PD software for genetic optimization
Organization: Rechnerbetrieb Informatik  /  RWTH Aachen
Date: 19 Nov 92 07:36:08 GMT

wm@spcklx.mpifr-bonn.mpg.de (Wolfgang Mauder) writes:

>I am looking for references and for PD software for function optimization using genetic algorithms. Who can help?

>Thank you

Hi Wolfgang,
your mail address seems to be incorrect - my mail bounced. So let it
do this way. Following copied from the FAQ of comp.ai.neural-nets.

> There is a GA mailing list which you can subscribe to by
> sending a request to GA-List-Request@AIC.NRL.NAVY.MIL
> You can also try anonymous ftp to
>   ftp.aic.nrl.navy.mil
> in the /pub/galist directory. There are papers and some software.

Hope this helps, Josef

-- 
Josef Nelissen, Lehrstuhl fuer Angewandte Mathematik, insbesondere Informatik,
RWTH-Aachen, Ahornstr. 55, W-5100 Aachen, Germany, Tel. +49 241/80-21060,
FAX +49 241/80-21079, e-mail jonas@beor.informatik.rwth-aachen.de
* You gave your life to become the person you are right now. Was it worth it? *
Newsgroups: comp.ai.neural-nets
From: dbrown@newssun.med.miami.edu (Daniel Brown)
Subject: principal component analysis and "wavelets"
Organization: University of Miami, School Of Medicine
Distribution: usa
Date: Tue, 17 Nov 1992 18:58:52 GMT

I posted about a week ago with a question about preprocessing
of image input.  I only received a few responses, and I need
more information on them:

	i) What is principal component analysis (PCA)?
	ii) Has anyone heard of "wavelets" used in 
	   preprocessing of image data?

Thanks in advance,

Dan Brown
dbrown@newssun.med.miami.edu

I will post a summary of replies ...
From: kern@arecibo.aero.org (J.C. Kern)
Newsgroups: comp.ai.neural-nets
Subject: Polynomial Networks or AIM?
Date: 18 Nov 1992 20:49:42 GMT
Organization: The Aerospace Organization
Distribution: world
NNTP-Posting-Host: kern.aero.org

The AbTech Corporation has a polynomial network tool called AIM that creates
polynomial equations to represent data relationships.  It trains similarly
to neural networks, but the resultant output are polynomials.  Has anyone
had experience with this technology or this product?  And, if so, how do
you feel about its viability and applicability?  I'll post a summary.



J.C. Kern                            email:  kern@arecibo.aero.org
The Aerospace Corporation            phone:  (310) 336-3573
P.O. Box 92957                       vmail:  (310) 336-5454 x63573
Los Angeles, CA 90009-2957    

                 "May you live in interesting times."
From: ijt@cm.cf.ac.uk (I J Taylor)
Newsgroups: comp.ai.neural-nets
Subject: ART Neural Networks
Date: 16 Nov 92 15:28:42 GMT
Organization: University of Wales College of Cardiff, Cardiff, Wales, UK.

Hi,

	We have a research group in Cardiff that are using ART networks to
analyse and compose music.  I am using ARTMAP at the moment with two ART 2
modules and am also developing an ARTMAP using two ART2a modules to increase
efficiency.  I am interested in making contact with other people who are using
similar networks to discuss results and overcome problems etc...

	Thanks,

				Ian Taylor
		email:      ijt@uk.ac.cf.cm
     
Newsgroups: comp.ai.neural-nets
From: moose@carina.unm.edu (Doug Warner)
Subject: Request: Citations for Strategies/Planning within a Context
Date: Wed, 18 Nov 92 00:30:56 GMT
Organization: Psychology Dept., University of New Mexico, Albuquerque
Followup-To: comp.ai.neural-nets

I am interested in references pertaining to Neural Networks and
Strategy or Planning within Context or Reference Frames.  I am doing a
literature review for my masters thesis and I am interested in training
networks within an offensive/defensive/neutral strategy in a
competitive environment.

Please do not post.  E-Mail me and I will summarize.

Doug Warner
moose@hydra.unm.edu
Newsgroups: comp.ai.neural-nets,uw.neural-nets
From: joordens@watserv1.uwaterloo.ca (Steve Joordens)
Subject: How deep are spurious attractor basins?
Keywords: Hopfield, Boltzmann, Attractors, Energy Functions
Organization: University of Waterloo
Date: Tue, 17 Nov 1992 14:36:55 GMT

   In recent simulations that I have been doing with a Hopfield net
model of human memory representation, the network has problems staying
out of spurious attractors.  I have heard about two possible ways of
solving this problem but, before applying this solutions blindly, I
want to know more about the problem itself.  My original thought was
that since spurious attractors were attractors that were not directly
'taght' to the network, they would be shallower (speaking in energy
space terms) than learned attractors.  If this is the case, then adding
a simulated annealing process may increase performance.  However, I 
have heard that this may not be the case (i.e., that spurious attractors
may be as deep as learned attractors).  So, the question of the day:

  Has anybody done any research comparing the depth of spurious and
  learned attractors using either a hopfield net ot boltzmann machine
  type architecture (references would be welcomed) ?

 uu     uu  ww        ww     Steve Joordens               ||      |\^/|      || 
 UU     UU  WW        WW     Department of Psychology     ||  ._|\|   |/|_.  ||
 UU     UU   WW  WW  WW      University of Waterloo       ||   \  Canada /   ||
  UU   UU     WW WW WW       Office: PAS 4259             ||   <____ ____>   ||
   UUUUU       WW  WW        (519) 885-1211 (ext 6819)    ||        |        ||
Newsgroups: comp.ai.neural-nets
From: arms@cs.UAlberta.CA (Bill Armstrong)
Subject: Re: How to train a lifeless network (of "silicon atoms")?
Organization: University of Alberta, Edmonton, Canada
Date: Mon, 23 Nov 1992 03:21:37 GMT

block@dxlaa.cern.ch (Frank Block) writes:
 

>In article <1992Nov21.002654.13198@news.columbia.edu>, rs69@cunixb.cc.columbia.edu (Rong Shen) writes:

>|> 	Please allow me to ask you this childish question:
>|> 
>|> 	Suppose you have a neural network and you want to train it to
>|> perform a task; for the moment, let's say the task is to recognize
>|> handwriting. Now suppose the network has recognized the word "hello,"
>|> and the weight in the synapse between neurodes (neurons) X and Y is k.
>|> If you proceed to train the network to recognize the word "goodbye"
>|> (by back propagation, or whatever algorithms), and since all the
>|> neurodes are connected in some way (through some interneurons, maybe),
>|> the synaptic weight between X and Y is likely to change from k to some
>|> other number; similarly, the weights in other synapses will change.
>|> Therefore, it is extremely likely that one training session will erase
>|> the efforts of previous sessions.
>|> 
>|> 	My question is, What engineering tricks shall we use to
>|> overcome this apparent difficulty?
>|> 
>|> 	Thanks.
>|> 
>|> --
>|> rs69@cunixb.cc.columbia.edu
>|> 
>--

>What you normally do during training is to present (taking you example) the
>words 'hello' and 'goodbye' alternatively. You should not train the net first
>just on one and then, when it has learned to recognize it, on the other.
>The training is a statistical process which in the end (let's hope) converges
>to a good set of weights (a compromise which recognizes all patterns in an
>optimal way).
>The engineering trick is mostly the so-called 'gradient descent' (in backprop). 
>This moves you current weight vector always in a direction which decreases the 
>network error measure.

>Hope this helps a bit

>Frank

I attended a tutorial once by Bernard Widrow, and he referred to the
"least disturbance principle".  The idea is to correct an error in
such a way that the overall state of the net is the least perturbed.
This is another way of looking at backprop.  Not only does it reduce
the error (in theory anyway), but it does so by favoring changes of
those weights which have the greatest effect on the error.  Hence it
can change those by the least amount to achieve a given amount of
error correction.

Widrow's profound insight into adaptation is shown in this simple
principle of his.  It has helped me a lot in thinking about adaptation
algorithms for ALNs too, but the scope of its applicability seems much
broader.

Bill
--
***************************************************
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: shashem@cornsilk.ecn.purdue.edu (Sherif Hashem)
Subject: Re: NN analysis
Organization: Purdue University Engineering Computer Network
Date: Mon, 16 Nov 1992 03:10:30 GMT

In article <1dvfvkINN2tv@manuel.anu.edu.au> shuping@andosl.anu.edu.au (Shuping RAN) writes:
>Hello,
>
>    Currently I am trying to understand how a trained NN performed 
>the given task. What is its internal funtionality, whether its internal 
>parameters relate to the real parameters of the given problem in some
>way.
>
>    Could some one give me some ideas of how to analyse a trained NN, 
>or some references.
>Thank you in advance,
>
>-Shuping RAN

In the area of using NNs for function approximation, C. Klimasauskas
wrote an article in Dr. Dobb's Journal, April 1991, 16-24, by 
the title: Neural Nets Tell Why.

Also I used sensitivity analysis to study the input-output relationships
learnt by a trained network. I published part of my work in a paper titled: 
"Sensitivity Analysis for Feedforward Artificial Neural Networks 
with Differentiable Activation Functions," in the proceedings of the`
1992 International Joint Conference on Neural Networks in Baltimore,
IEEE, NY, I:419-424.

Sherif Hashem

School of Industrial Engineering
1287 Grissom Hall, Purdue University
West Lafayette, IN 47907-1287.
shashem@ecn.purdue.edu
(317)494-0440 (O), (317)494-1299 (Fax).
--
Sherif Hashem

School of Industrial Engineering
1287 Grissom Hall, Purdue University
From: bat@dstos3.dsto.gov.au
Newsgroups: comp.ai.neural-nets
Subject: Re Making a group for genetic algorithms
Date: 17 Nov 92 14:50:17 CST
Organization: Defence Science and Technology Organisation

A group for GA's ?

		   count me in !!!!!!!!

				Brenton Thomas
Newsgroups: comp.ai.neural-nets
From: dbrown@newssun.med.miami.edu (Daniel Brown)
Subject: Genetic Algorithms
Keywords: GA
Organization: University of Miami, School Of Medicine
Date: Thu, 19 Nov 1992 14:08:13 GMT

Can somebody please post a short explanation of what 
exactly GAs are and how they are related to neural nets?

Thanks,

Dan
dbrown@newssun.med.miami.edu
From: speeba@thor.cf.ac.uk (Eduardo Bayro)
Newsgroups: comp.ai.neural-nets
Subject: Job opportunity in Vision and A.I.
Date: 21 Nov 92 19:49:38 GMT
Organization: University of Wales College of Cardiff, Cardiff, Wales, UK
X-Mailer: Cardiff Computing Maths PP Mail Open News Gateway

Hello Dear Netters!

I will finish my PhD this December and I am looking for an opportunity 
to work in a research laboratory in industry or at an University  in
countries such as Germany, Switzerland, Spain, Holland or Belgium.
 I am interested in areas related to Machine Vision and Artificial Intelligence, for example:  Industrial Visual Inspection (my work involved the intelligent
 visual inspection of rubber moulded car seals), Robot Vision, Medical Image
 Analysis, and Satellite Image Interpretation. Could anybody send me  post
 or e-mail addresses of organisations involved in these research areas. Thanking you in advance, Eduardo.
Address: Eduardo Bayro C. e-mail: speeba@uk.ac.cardiff
U.W.C.C., Dept. ELSYM, P.O.Box 904, Cardiff Cf1 3YH, U.K.

-- 
 Eduardo Bayro, School of Electrical, Electronic and Systems Engineering,
 University of Wales College of Cardiff, P.O. Box 904, Cardiff, CF1 3YH, UK.
 Internet: Bayro@Cardiff.ac.uk                    Janet:  Bayro@Uk.ac.Cardiff
 UUCP:     Bayro@Cardiff.UUCP or ...!mcsun!ukc!Cardiff!bayro
From: steve@fulcrum.oz.au (Steve Taylor)
Subject: Re: How to train a lifeless network (of "silicon atoms")?
Organization: The Fulcrum Consulting Group
Date: Mon, 23 Nov 1992 23:21:29 GMT

rs69@cunixb.cc.columbia.edu (Rong Shen) writes:

>Your Highness:
Hey, I like it.

>	Suppose you have a neural network and you want to train it to
>perform a task; for the moment, let's say the task is to recognize
[... deleted ...]
>Therefore, it is extremely likely that one training session will erase
>the efforts of previous sessions.

>	My question is, What engineering tricks shall we use to
>overcome this apparent difficulty?
One training session will undo previous sessions. The trick is to interleave
your sets of training data so the net never has time to concentrate on
solving just part of your problem.

It's definitely in the "feature, not bug" category too, as it means that a nn
can adapt itself to a new problem when required.

>rs69@cunixb.cc.columbia.edu

Steve
steve@fulcrum.oz.au
From: scottd@cs.hw.ac.uk (Scott Dunn)
Newsgroups: comp.ai.neural-nets
Subject: RCS - units with multiple sites?
Date: 22 Nov 92 13:44:23 GMT
Organization: Dept of Computing & Electrical Engineering, Heriot-Watt University, Scotland

I have been looking at the Rochester Connectionist Simulator as an
example of how to design a flexible and open NN simulator.

The one big problem I have is with with sites.  Precisely what purpose
do they serve.  I know what the manual says - "allows differential treatment
of inputs" but there are no examples in the manual with multiple sites.

IMHO, allowing for lack of experience, they complicate the error determination
without adding much in the way of flexibilty - I am obviously missing something,
probably fairly obvious, so, could someone please explain further.

Cheers,

Scott.
Newsgroups: comp.ai.neural-nets
From: a0mali01@orion.spd.louisville.edu (Aleksander Malinowski   Olek)
Subject: Neural net text, finally!
Summary: I have found a very good textbook about artificial neural systems.
Date: Fri, 20 Nov 1992 02:29:20 GMT
Organization: University of Louisville
Keywords: neural network, textbook

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 .
...........................................................................
-- 
                       	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: elelko@nuscc.nus.sg (Free Man)
Subject: Fn approxn - ref
Organization: National University of Singapore
X-Newsreader: Tin 1.1 PL4
Date: Mon, 16 Nov 1992 09:29:37 GMT


I am looking for a paper by G.Cybenko entitled 'Approximation by
superpositions of a sigmoidal function'. It's published in Mathematics
of control, signals and systems (1989), 2. The stinker is my uni lib
does not have the publication, and neither do the libs within the
region. So the next best thing they have to offer is a lengthy
repographic search request .. 

Can anyone help, like getting in touch with the author thru the internet
?

Rgds,
K.O.Looi
NUS, S'pore
Newsgroups: comp.ai.neural-nets
From: clin@eng.umd.edu (Charles Lin)
Subject: Looking for Romaniuk(?) from U. of South Fl.
Date: Wed, 18 Nov 92 20:16:09 GMT
Organization: College of Engineering, Maryversity von Uniland, College Park
Originator: clin@state.eng.umd.edu


   I am looking for the e-mail address of Dr. Romaniuk (?) from the
University of South Florida.   This regards a hybrid system that
he and a colleague wrote a paper on.   E-mail me at clin@eng.umd.edu

--
Charles Lin
clin@eng.umd.edu