HDTenEightyP/NetNews huggingface dataset:
From: al@jargon.gmd.de (Alexander Linden)
Subject: Re: C++ class library for NN
Keywords: C++, NN
Organization: GMD, Sankt Augustin, Germany
Date: Sat, 12 Sep 1992 09:52:14 GMT

palachar@bert.eecs.uic.edu (Prasad Palacharla) writes:

>Hello world:

>I request you to point out where can I access / purchase any C++ class libraries or software tools for
>implementing neural network models.

We have designed and implemented SESAME, which is written in GNU C++,
- the newest version contains about 70 classes, 
- it is building blocks oriented,
- the level of abstractions of the building blocks is arbitrary,
- building blocks can be 
  - neurons (which is quite inefficient and intransparent)
  - layers of nodes and weights 
  - neural net models 
  - topology of neural nets (recursivly)
- graphics is fully integratable (based on InterViews)
- autoconfigures experiments
- it supports Kohonen Feature Maps, Backpropagation, 
- we implemented also Reinforcement Learning, we did many experiments
  with character recognition and speech. Currently our main focus of
  interest is neurocontrol and robotics.
- the simulator supports combinations of different application and
  pattern processing paradigms.
- and it is possible to have some kind of runtime inheritance, by
  including new properties to neural network models.


If you are interested, you can have a look on that piece of software
(its an older version, a new version comes out in a few weeks).  It
comes without warranty, the status of the software is experimental.
The documentation is a bit poor.  It is a prototype and we are
unfortunately not able to give sufficient support.

ftp ftp.gmd.de
anonymous
(e-mail) as password
cd gmd/as/sesame
get README

here you will find also our abstract which has been accepted for poster
presentation at NIPS-92 in Denver.

nips92-abstract.ps.Z "An Object-Oriented Framework and its Implementation
                      for the Simulation of Neural Nets"
sesame.tr1.ps.Z
sesame.tr2.ps.Z     contains more technical reports
sesame.docu.ps.Z    contains the rudimentary documentation.

Please join us to think with about how to proceed with SESAME.
May be it should be something similar to the GNU project.

If you want contact us, send email to sesame-request@gmd.de.


Cheers,

Alexander Linden

Alexander Linden             | TEL. (49 or 0) 2241/14-2435, FAX. -2618 or -2889
GMD - AI Research Division   | TELEX 889469 gmd d
P. O. BOX 1316               | email:  A.Linden@gmd.de
D-5205 Sankt Augustin 1, FRG


--
Alexander Linden             | TEL. (49 or 0) 2241/14-2435, FAX. -2618 or -2889
GMD - AI Research Division   | TELEX 889469 gmd d
P. O. BOX 1316               | email:  A.Linden@gmd.de
D-5205 Sankt Augustin 1, FRG
Organization: Bar-Ilan University Computing Center, Israel
Date: Thu, 10 Sep 1992 00:13:56 IST
From: Victor Earl Aker <S24615@BARILVM.BITNET>
Newsgroups: comp.ai.neural-nets
Subject: request for info on ANN's to be used for speech/phenome recognition

  I would be greatly appreciative if anyone could send me information on the
theories of ANN's that are used for speech/phenome recognition. Since I am
quite new and am not intimately familiar with the theories behind ANN's, any
and all knowledge, programs would be greatly appreciated.
  I hope that my request makes sense, and I thank you all in advance.

Victor Aker
2nd year college Student,
British Columbia Institute of Technology.
(Alright, so I am in Israel right now. I'm on holidays!)
From: ajr@eng.cam.ac.uk (Tony Robinson)
Newsgroups: comp.ai.neural-nets
Subject: Re: NNs in chess or other games
Date: 15 Sep 92 22:01:49 GMT
Organization: Cambridge University Engineering Department, UK

In comp.ai.neural-nets bruhn@uxe.wu-wien.ac.at (Peter Bruhn) writes:

>Does anyone of you know something about an application of neural nets
>in strategic games (esp. chess). What I think about is that a NN could
>be trained to evaluate a position or some aspects of a position (e.g.
>the structure of your pawns, how safe is the position of your king,...).
>
>.... Once a chess master (I don't remember his
>name) was asked how many moves he prefigures in advance and he answered
>"normally: none".

There has been some interesting work done in this area.  Perhaps the best
complete system is for backgammon and is described in:

  Gerald Tesauro, "Practical Issues in Temporal Difference Learning",
  Machine Learning, vol 8, no 3/4, May 1992.

The basic idea is to learn a connectionist position evaluator though self
play.  Supporting this are ideas from control theory, especially the backing
up of lookahead using dynamic programming.  A good reference is:

  Andrew G. Barto and Steven J. Bradtke and Satinder P. Singh,
  "Real-Time Learning and Control using Asynchronous Dynamic Programming",
  Department of Computer Science, University of Massachusetts, Amhurst MA,
  no 91-57, Aug 1991.

Also, vol 8 of Machine Learning 1992 is a special issue on reinforcement
learning and would be a good place to start.

Tony [Robinson]
From: dsimon@atlas.nafb.trw.com
Newsgroups: comp.ai.neural-nets
Subject: Re: Neural Net in Communications
Date: 15 Sep 92 12:08:53 PST

> I am looking for a small project to do in neural networks and I wanted 
> to know if there are any communication applications I can use them in. 
> Any references, please post or mail at above address

How about Phase-Locked Loops?  I'm thinking about trying this myself,
but I'm just at the arm-waving stage right now. 
If someone else works on it too, it would be interesting to compare
results.  A PLL tracks the phase of an incoming signal.  But if we
have a priori information about the phase of the incoming signal, then maybe
we could train a neural net to track the phase better than a conventional
PLL.  I can gladly go into more detail if you're interested.

- Dan Simon -
Xref: sparky comp.ai.neural-nets:3479 sci.image.processing:691
From: laws@ai.sri.com (Kenneth I. Laws)
Newsgroups: comp.ai.neural-nets,sci.image.processing
Subject: Re: Neural Nets applied to GIS?
Date: 7 Sep 92 15:18:11 GMT
	<1992Aug28.131433.3771@infonode.ingr.com>
Followup-To: comp.ai.neural-nets
Organization: Computists International
In-reply-to: greg@monty.b17b.ingr.com's message of 28 Aug 92 13:14:33 GMT

In article <27AUG199213170171@apsicc.aps.edu>, einhorn@apsicc.aps.edu
 (The Einhorn Family) writes:
|> Has anybody used neural networks to examine remote sensing images
|> and map layers in a GIS (Geographical Information System)?  We are
|> hoping to use them to find archaeolgical sites, but any application
|> to GIS problems is of interest.

There's a company called Audre Recognition Systems Inc. that
specializes in software and workstations for converting maps
and drawings to vector or CAD formats.  They claim to use
neural networks as part of their user-defined-symbol recognition.
I suspect that the NN R&D was done at Carnegie Mellon under
a proprietary contract.

The contact info is Audre Recognition Systems Inc., 10915
Technology Place, San Diego, CA 92127, (619) 451-2260.
I doubt that they have a net connection.

                                        -- Ken
--

Dr. Kenneth I. Laws, (415) 493-7390, laws@ai.sri.com.
Moderator of the Computists International AI/IS/CS mutual-aid association.
Ask about my weekly online career newsletter, The Computists' Communique.
Newsgroups: comp.ai.neural-nets
From: int565c@ccds.cc.monash.edu.au
Subject: Help: Networks for classification problem
Rely-To: int565c@ccds.cc.monash.edu.au
Organization: Monash University
Date: Sat, 5 Sep 1992 13:32:17 GMT

I am a novice in the this area. I have the questions to pose:

1) What neural networks are suitable for classification problem. I know
   backpropagation is one of such networks but I would like to know if there
   are any networks suitable for this sort of task.

2) Are Kohonen network and Hopfield network suitable for the same task.
   Specifically, I need a few neural networks to classify texture images based
   on their texture features.

3) Are there any neural network systems (such as Aspirin/MIGRAINE) available
   via ftp? If there are, what are the addresses?

I have been using Aspirin/MIGRAINE nntools but it has only backpropagation
model. Now, I need to extend my classification engine to other network models
and require some network system as an aid. My primary interest is not to
analyse or to build the network models but to use them (as is) for the
classification problem in hand.

Thanks in advance.

Edward
______________________________________________________________________________

e-mail: edwarda@ccds.cc.monash.edu.au                            _--_|\
Department of Computer Science                                  /      \
Monash University (Clayton Campus)                              \_.--. /
Australia                                                             V
______________________________________________________________________________



Newsgroups: comp.ai.neural-nets
From: edwin@maui.cs.ucla.edu (Edwin Tisdale)
Subject: Re: function approximation with neural nets : what is right ?
Organization: UCLA Computer Science Department
Date: Thu, 10 Sep 92 18:20:15 GMT

In article <1992Sep10.153652.12503@noose.ecn.purdue.edu>
kavuri@lips2.ecn.purdue.edu (Surya N Kavuri ) writes:
>  
>   In using a neural net to approximate a function, it is done with
>   out any a priori information on what class of functions we are 
>   looking for.  For example, if I know the function is some 
>   polynomial, there is no way to impose this condition on the net.
>
If you **know** that the function you are trying to imitate is best
approximated by a polynomial then you **should** use a polynomial.
The polynomial is just another kind of network.  The non-linear
units are just the powers ( x^0, x^1, x^2, ...) of the input variable.
People sometimes call these things Sigma-Pi units.
The weights are just the coefficients (a_0, a_1, a_2, ...) which can
be estimated using conventional polynomial regression.
The problem is a little harder if you don't have much data to "train"
with and you don't know which (or even how many) terms (units) to
include in your network.  You might try using variable exponents (x^w).
But then you need a learning algorithm to propagate errors backward to
the exponents.  In short, you should always try to use the network
architecture which is most appropriate for your application.  But
unless you have some good reason for believing one is better than
another, any of the black-box models will do.  And you should just
try the one that is most convenient.  Hope this helps, Bob Tisdale
UCLA-CSD.
Newsgroups: comp.ai.neural-nets
From: ian@cs.UAlberta.CA (Ian Parsons)
Subject: Report available: ALNs quantifying Tar sands feedstock.
Summary: Report available anonymous ftp: Adaptive Logic Neworks application.
Keywords: applications, adaptive logic networks
Organization: University of Alberta, Edmonton, Canada
Date: Thu, 10 Sep 1992 17:12:18 GMT


Hello,

A report on an application to quantify tar sands feed using near-infrared
spectroscopy and adaptive logic networks is now available using
anonymous ftp at menaik@cs.ualberta.ca.  Comments and input are welcome.
The file is located in the directory pub and is called alntarsands.ps.Z.

The abstract:

The usefulness of Adaptive Logic Networks (ALNs) to analyse Near-Infrared
(NIR) spectra of tar sands feedstock in a plant situation is examined. 
Laboratory experiments with tar sands do show good correlation between NIR
and physical analysis using traditional mathematical and back propagation
neural network techniques.  The plant results are inconclusive due to poor
correlation between the spectra and the physical grab samples.  Regardless
which of the three correlation techniques used the error of prediction is
similar.  A priori knowledge reduced the data set needed for input from
585 variables to 25.  The reduction did not compromise the resolution of the
spectra and is based on the shape of peaks.  Training and testing of ALN trees
are fast despite the large number of nodes.  ALN trees can be easily converted
to hardware which is needed in a harsh plant environment.  More work is
currently being done to reduce the error between the observed spectral results
and the physical analysis.  

All comments and input should be emailed to me as I don't have time to
read this group as regularly as I should.  I worked for Western Research
Centre (WRC) for 12 years so finding this project was easy.  Finding quick
and reliable methods to quantify tarsands feedstock is an ongoing research
area at WRC.  I developed the code using Atree Release 2.0 on a Unix platform
(Sparc).

email: ian@cs.ualberta.ca

Ian Parsons
Univerity of Alberta

ps. The file should be labeled aln.tar.ps.Z but too many people will get
    confused :)
From: arras@cascor.forwiss.uni-erlangen.de (Mike Arras)
Subject: Re: search for literature on recurrent networks
Organization: FORWISS, Uni Erlangen, Germany
Date: Sat, 5 Sep 1992 11:43:46 GMT
Keywords: recurrent, networks


In article <1992Sep4.155607.4536@a.cs.okstate.edu>, siva@a.cs.okstate.edu (KAVUTURU SIVA RAMA) writes:
>        I am searching for the literature on recurrent networks. If you 
>        have any information regarding recurrent networks please send me.


	The first article is good.  The second I have not read.

128.146.8.52    archive.cis.ohio-state.edu      % neuroprose
in pub/neuroprose

pearlmutter.dynets.ps.Z
from barak.pearlmutter@cs.cmu.edu
Survey of learning algorithms for recurrent neural networks with
hidden units, with some new results and simulations.

kehagias.srn1.ps.Z
from ST401843@brownvm.brown.edu
Stochastic Recurrent Networks (collections of interconnected finite
state units) are introduced; they can be trained very efficiently
by the local Backward-Forward Algorithm, a variant of the Hidden
Markov Model Backward-Forward training algorithm.

	This is also a good paper, but it's covers one specific arch..

128.2.254.155	pt.cs.cmu.edu			% cascor site
in /afs/cs/project/connect/tr

rcc-tr.ps.Z
by Scott Fahlman
Recurrent Cascade-Correlation paper.

	There are definately more.  These, however, can be quickly
obtained via ftp.
                                    __    __
Michael Arras                      /  \  |  |  Bavarian Research
arras@forwiss.uni-erlangen.de     /    \ |  |  Center for
Artificial Neural Network and    /  /\  \|  |  Knowledge-Based
Fuzzy Logic Research Group      |   __   |  |  Systems__________
Tel (09131) 691-211             |__|  |__|__|  
I speak for me, myself and I.   F O R W I S S  ERLANGEN * MUNICH * PASSAU
From: mosnier@erato.enst.fr (Francois Mosnier)
Newsgroups: comp.ai.neural-nets
Subject: back-prop versus signal-processing
Keywords: neural-nets - trigonometry - back-propagation
Date: 15 Sep 92 18:42:36 GMT
Organization: Telecom Paris, France

A friend of mine is trying to teach a neural network via back propagation, a very
easy problem : it is how to convert w (rotational speed) and v (axial speed) to
Delta-X and Delta-Y, the mean Delta-t being given.

In fact, he's meeting (I hope it won't last) a lot of problems, and he's worrying
about the best activation functions.  cos() and sin() could be a natural(1) idea.
But it does not work better than tanh() - i.e. very bad.

	- Is there someone having idea on this subject ?

	- Is there a good reason why a neural-net with a single neuron, with a
	   a linear activation function CANNOT learn addition ?

	- Are NN only good at sweeping out the noise on a signal ?


			Thank's for all contributions.

			      ____
			|\  /|
			| \/ |-           
			|    |			Francois Mosnier
				

		
(1) for a non-specialist, as I'am.


+------------------------------------o-------------------------------------+
| P-mail:                            | E-mail:                             |
|   255 Av Daumesnil - Appt 211      |        mosnier@inf.enst.fr	   |
|   75012 Paris - France             |        mosnier%enst.fr@uunet.uu.net |
|   (1) 43 47 10 57		     |                                     |
+------------------------------------o-------------------------------------+

	
Newsgroups: comp.ai.neural-nets
From: gaynier@elec.uq.oz.au (Ron Gaynier)
Subject: Looking for Williams & Zipser
Summary: I need a paper quickly
Keywords: Address request
Organization: Prentice Centre, University of Queensland
Date: Thu, 10 Sep 1992 23:52:40 GMT


Hello,
Would anyone know how I might quickly get in touch with either
Ronald Williams or David Zipser (i.e. email addresses?) I am in
desparate need of a copy of a paper they published in "Connection
Science" V1 pp87 1989, title unknown. It concerns details of simulations
they ran on their algorithm for recurrent networks. I developed a
learning algorithm for recurrent networks with Heaviside neurons and
I want to compare my results with theirs.

Any help would be appreciated as I need the paper quickly so I can
get a paper I'm writing ready for submission to a conference.

I've exhausted the standard procedures for finding it, otherwise I
wouldn't bother the network.

Thanks

Ron Gaynier
From: hossein@ces.cwru.edu (HOSSEIN)
Newsgroups: comp.ai.neural-nets
Subject: Looking for information about Synaptics I-1000 chip
Date: 15 Sep 92 01:50:11 GMT
Organization: Case Western Reserve University
NNTP-Posting-Host: ida.ces.cwru.edu

Hi,
	I am looking for any information about the Synaptic I-1000
neural network chip. Would you send me some?

Thanks for your help,
Seyed.
Newsgroups: comp.ai.neural-nets
From: sef@sef1.slisp.cs.cmu.edu
Subject: Re: Looking for Williams & Zipser
Organization: School of Computer Science, Carnegie Mellon
Date: Fri, 11 Sep 1992 19:10:34 GMT


    From: nilsch@eik.ii.uib.no (Nils Chr. Hertzberg)
    
    What are Heaviside neurons? Just curious...
    
This is a term used by some EE and control theory people for "sharp
threshold units", as used in the classical perceptron.  This is the limit
of a sigmoid unit if you scale all the weights (and the bias or threshold)
up by some very large amount.

-- Scott

===========================================================================
Scott E. Fahlman
School of Computer Science
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213

Internet: sef+@cs.cmu.edu

Newsgroups: comp.ai.neural-nets
From: nilsch@eik.ii.uib.no (Nils Chr. Hertzberg)
Subject: Re: Looking for Williams & Zipser
Keywords: Address request
Organization: Institute of Informatics, University of Bergen, Norway
Date: Fri, 11 Sep 92 13:00:45 GMT


In article <gaynier.716169160@s1.elec.uq.oz.au>, gaynier@elec.uq.oz.au (Ron Gaynier) writes:
|> 
|> learning algorithm for recurrent networks with Heaviside neurons and
                                                  ~~~~~~~~~

What are Heaviside neurons? Just curious...

Nils
-- 
__________________________
Nils Christian Hertzberg
Department of Informatics
University of Bergen
NORWAY
Newsgroups: comp.ai.neural-nets
From: remek@mamura.ee.mu.oz.au (Remek Lipinski)
Subject: Re: Matlab source code for backpropagation algorithm
Keywords: matlab, mlps, feedforward, gradient descent
Organization: Electrical and Electronic - University of Melbourne 
Date: Sat, 5 Sep 1992 03:02:23 GMT

In article <Sep.3.15.16.40.1992.6047@constitution.rutgers.edu> naik@constitution.rutgers.edu (Devang Naik) writes:
>
>Hello netter,
>
>	A couple of months back, someone posted a matlab code for
>	the backpropagation algorithm on the net. I would appreciate
>	if someone could send me the source code. Thanks a lot in
>	advance, 

if possible culd you please post it again, I would like a copy as well,thanks.
===============================================================================
** Remek Lipinski   :  remek@mullian.ee.mu.OZ.AU                              **
**                                              "I'll be back!"  - T800      **
===============================================================================
From: Stefano_De_Luca@mas.sublink.org (Stefano De Luca)
Newsgroups: comp.ai.neural-nets
Subject: AI & Renal Insufficence: help wanted
Date: 14 Sep 92 03:08:29 GMT
Organization: MangaZone Advanced Services, Rome - ITALY


I am working with a research team at Rome University, Medicine.

I am strongly interested in any kind of AI work into renal insufficence and
pharmacological therapy management in dialized patients.

We are planning an Expert System considering, for every drug, the exact
dose relating the current parameters of renal functionallity and the same
problem in the dialisys context.

Do you know about other work on this problem? Any pointer?

Do you think neural nets are a better choice to manage this problem?

Thank you by
        stefano de luca

________________________________________________________
stefano de luca   stefano_de_luca@mas.sublink.org
via di Tor Paluzzi, 83/d - 00040 Cecchina (Rome) - Italy



 -- AEd V1.36/Eval

-- Via DLG Pro v0.992
Xref: sparky comp.ai.neural-nets:3421 sci.aeronautics:1934
From: dd@doc.ic.ac.uk (Demetrius Dracopoulos)
Newsgroups: comp.ai.neural-nets,sci.aeronautics
Subject: McDonnell Douglas and Neural Networks
Date: 7 Sep 92 19:30:10 GMT
Reply-To: dd@doc.ic.ac.uk (Demetrius Dracopoulos)
Organization: Department Of Computing, Imperial College, London UK
NNTP-Posting-Host: swan.doc.ic.ac.uk

Hello all,

I am looking for any references or information regarding work done in McDonell
 Douglas using Neural Nets.

Thanks in advance

Dimitris Dracopoulos
Dept. of Computing
Imperial College
180 Queens Gate
London SW7 2BZ
UK


-------------------------------------------------------
Don't mess with Texas
-------------------------------------------------------

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**  **    **    *** ***    **     **      **  **     **
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Newsgroups: comp.ai.neural-nets
From: jdyvig@kirk.uucp (Jim Dyvig)
Subject: Re: Looking for Williams & Zipser
Organization: Photon Research Associates, Inc.
Date: Fri, 11 Sep 1992 17:21:02 GMT


He can be contacted at the CogSci dept at UCSD

try zipser@cogsci@ucsd.edu or just zipser@ucsd.edu

Jim Dyvig
Photon Research Associates
jdyvig@photon.com
From: jmlake@grimm.cogsci.uiuc.edu (J M Lake)
Subject: Re: NNs in chess or other games
Organization: University of Illinois at Urbana
Date: Tue, 15 Sep 1992 16:41:13 GMT

ed@northstar.Stanford.EDU (Edward Wilson) writes:

>On a related topic, you might want to check out some papers by Arthur Samuel
>(Bell Labs), one printed in MIT's Technology Review in 1959.  He was working
>with slightly more primitive computers, so he worked on trying to *teach* a
>computer to play checkers.  Teach is the key word here.


A full (original) reference for that is

	Samuel, A. L.  Some studies in machine learning using the game of checkers.
	IBM Journal of Research and Development 3(3), 210-229.

Mike
--
J. Michael Lake          Beckman Institute for Advanced Science and Technology
Dept. of Comp. Science           jmlake@uiuc.edu             Urbana, IL  61801
From: surana@schmied.ecn.purdue.edu (Pankaj K Surana)
Newsgroups: comp.ai.neural-nets
Subject: GA+NN=AI
Keywords: genetic algorithms, neural nets
Date: 15 Sep 92 19:54:13 GMT
Organization: Purdue University Engineering Computer Network

Here's a question: Is there any literature that discusses
the possible use of genetic algorithms in conjunction with
neural networks. 

I have been thinking that, perhaps, a genetic algorithm that
manipulates the formation of neural nets could, through
evolutionary changes, discover a more appropriate neural
model for cognitive research. The "neuron" itself would remain
unchanged, but the number of and structure of interconnections 
between neurons would be changed according to some algorithm.

As another method, would it be possible for a neural structure
to be created and implemented "on the fly", that is, as a stream
of data is sent through it? Could the neurons align themselves
appropriately to handle different types of data, with different 
resultant effects?

Just a few questions thrown out to the group. Any thoughts should
be e-mailed to surana@en.ecn.purdue.edu

Thanks,

Pinku Surana