HDTenEightyP/NetNews huggingface dataset:
From: speeba@thor.cf.ac.uk (Eduardo Bayro)
Newsgroups: comp.ai.neural-nets
Subject: Paper on NN's and Vision
Date: 9 Sep 92 19:58:48 GMT
Organization: University of Wales College of Cardiff, Cardiff, Wales, UK
X-Mailer: Cardiff Computing Maths PP Mail Open News Gateway



Dear netters! 

We have had many requests for our paper "Neural Computing for Noise
Filtering, Edge Detection and Signature Extraction" by D.T. Pham and
E.J. Bayro-Corrochano. We have now run out of offprints. The paper has 
photos so we cannot offer a postscript copy via ftp.
The paper  is published in The Journal of Systems Engineering, 2(2), 111-122 , 
1992, Springer Verlag. 

Thank you for your interest in our work.

Eduardo.
 

-- 
 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: hannahp@topaz.ucq.edu.au
Newsgroups: comp.ai.neural-nets
Subject: Help: function estimating networks.
Date: 10 Sep 92 13:47:58 AET
Organization: University of Central Queensland, Australia


I have been playing around with networks for a year or so now (Kohonen,
small amounts of BP, Hopfield, Carpenter/Grossberg).  As you can tell,
I have been working with recognition/decision software.

I now require some information/references to a/some networks that can
operate in the following way :
             ______________
            |              |
(x,y,z)     |              | (a,b,c)
----------> |       N      | -------->
            |              |
            |______________|

i.e.    act as a function that maps (x,y,z) onto some (a,b,c)n


If anyone has any information about a neural network that could manage this,
of any references OR a reason why this cannot be done!  Please email me.


Thanks.

Paul Hannah.
From: ajr@eng.cam.ac.uk (Tony Robinson)
Newsgroups: comp.ai.neural-nets
Subject: Re: search for literature on recurrent networks
Keywords: recurrent, networks
Date: 5 Sep 92 10:01:06 GMT
Organization: Cambridge University Engineering Department, UK

In comp.ai.neural-nets 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.
>

A recent message to comp.ai.neural-nets gave details of how to access the
anonymous ftp site svr-ftp.eng.cam.ac.uk.  In directory "reports" you will
find seven reports on recurrent nets.  This includes my PhD thesis "Dynamic
Error Propagation Networks" (how I wish I'd used the word "recurrent") and a
recent turorial which has two pages on recurrent nets, giving about six
different forms of supervised net and three unsupervised nets.

Much of what a standard back-prop net does can be done with standard
statistical techniques, often better and faster (e.g. weighted nearest
neighbours, Gaussian mixtures, etc).  But there seems to be nothing to match
the processing power of a recurrent net classifier.

Tony [Robinson]
Newsgroups: comp.ai.neural-nets
From: ed@northstar.Stanford.EDU (Edward Wilson)
Subject: Re: NNs in chess or other games
Organization: stanford
Date: Mon, 14 Sep 92 17:04:20 GMT


I remember reading an article (the cover article, I believe) in Scientific 
American ~2 years ago regarding chess-playing computers (at CMU ?).  

It did not involve
NNs, but talked a lot about the computers and algorithms.  One interesting
graph I remember was one that had on the abscissa the number of moves ahead
that the best chess computers were able to look ahead (the earliest ones were
around 5 moves, and the latest ones were around 12-13 moves, I think).  On 
the ordinate, they had the chess rating for the chess computer (ratings for
chess players range from 1200 for beginners to 1800 for good players to 2300
for "Masters" to ~2700 (?) for the handful of best players in the world).
The interesting thing was that the graph, with ~10 data points was very linear -
implying a direct relationship between how many moves the computer can look
ahead and how good it is (ranking wise).

At the time of the article, the best computers were able to beat all but the 
very best in the world (handful of people).  I haven't heard anything since
then, but it seemed at the time that it was just a matter of being able to look
ahead about 2 or 3 more moves before the best could be beaten.

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.

Good luck, hope this helps.

Ed Wilson, Aerospace Robotics Lab, Stanford University
ed@sun-valley.stanford.edu
From: hewittj@topaz.ucq.edu.au
Newsgroups: comp.ai.neural-nets
Subject: Boltzmann Machine a la Patrick Simpson.
Date: 10 Sep 92 11:32:18 AET
Organization: University of Central Queensland, Australia



As part of a 3rd year computing project I have been trying to implement a
general Boltzmann Machine Neural Network for experimentation.

The algorithm I am using is from the book "Artificial Neural Systems" by
Patrick Simpson. Simpson's algorithm is the most complete example I have
found for the B.M (even if it does seem simplistic).
!! However I can't get it to work - no matter what I try (and believe me,
I have tried many algorithm variations).

I've read a lot of background material, however nearly all of it is NOT
very specific on the network architecture and algorithm details.

There must be someone out there who has successfully coded the algorithm
for the B.M.  Could someone  p l e a s e  suggest some practical readings/
examples that might be of help ?????

Thanks muchly in advance..
                          John Hewit.
From: prechelt@i41s14.ira.uka.de (Lutz Prechelt)
Newsgroups: comp.ai.neural-nets
Subject: Comparison of SuperSAB vs. Quickprop
Date: 14 Sep 1992 09:13:37 GMT
Organization: University of Karlsruhe, FRG
Distribution: world
NNTP-Posting-Host: i41s14.ira.uka.de


Has anybody compared the SuperSAB variant of Backpropagation 
(Tollenaere) with the Quickprop variant (Fahlman) ?

I would like to know about any experiences.
Or does somebody know about any paper where such a comparison is done
or mentioned ?

Here are the principal references to SuperSAB and Quickprop:

@article{Tollenaere90,
   author    = {Tom Tollenaere},
   title     = {SuperSAB: Fast Adaptive Backpropagation with Good 
                Scaling Properties},
   journal   = {Neural Networks},
   year      = {1990},
   Volume    = {3},
   number    = {},
   pages     = {561-573},
   month     = {},
   Class     = {nn, learning},
   abstract  = {},
   notes     = {^^, very good paper},
}

@techreport{Fahlman88,
   author    = {Scott E. Fahlman},
   title     = {An Empirical Study of Learning Speed in Back-Propagation
                Networks},
   institution = {School of Computer Science, Carnegie Mellon University},
   year      = {1988},
   Type      = {},
   number    = {CMU-CS-88-162},
   address   = {Pittsburgh, PA 15213},
   month     = sep,
   Class     = {nn, learning},
   abstract  = {Excellent paper.
                discusses "what is a good benchmark?", encoder/decoder task,
                complement encoder/decoder, XOR,
                termination of learning, how to report learning times,
                tuning of backprop learning parameters, derivation of quickprop },
   notes     = {^^, neuroprose},
}


Please reply by email, I will summarize.

  Lutz

-- 
Lutz Prechelt   (email: prechelt@ira.uka.de)            | Whenever you 
Institut fuer Programmstrukturen und Datenorganisation  | complicate things,
Universitaet Karlsruhe;  D-7500 Karlsruhe 1;  Germany   | they get
(Voice: ++49/721/608-4317, FAX: ++49/721/694092)        | less simple.
Newsgroups: comp.ai.neural-nets
From: tvuorita@cc.curtin.edu.au
Subject: WANTED: Original of article
Organization: Curtin University of Technology
Date: Thu, 10 Sep 1992 09:59:56 GMT

can anyone help me with an article I am trying to find. The reference is

Devos,M.R. & Oban,G.A. (1988). Self Adapting Backpropagation. In
Proceedings NeuroNimes 1988, EZ, Nanterre, France.

I am working on a  development of a paradigm which is in turn a development
of their work, but I do not have their original paper.

Any help in obtaining it would be much appreciated.

Neil Belford 
Newsgroups: comp.ai.neural-nets
From: andrzej@bnr.ca (Andrzej Bieszczad)
Subject: Re: Neural Net in Communications
Organization: Bell-Northern Research Ltd.,
Date: Mon, 14 Sep 1992 18:53:19 GMT

In article <4934@daily-planet.concordia.ca> asif@sunlite.concordia.ca writes:

   Hello :

   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

   Thanks in advance

   Asif

How about:

- adaptive filters (noise reduction),
- adaptive routing,
- call admission (ATM),
- link capacity assignment (ATM),
- real-time fault detection,
- knowledge base acquisition and refinement for NetMgmt Exp.Sys.,
- adaptive network configuration for mobile tel. nets,
- network engineering (TSP+++),
- probably many other possible uses.

I wrote a proposal on the subject to get my company to pay for my PhD
studies. They were not interested (so you might not find the material
interesting either; I will pay for the studies myself), so perhaps
somebody can use it. I cannot post, since that is too long, but I can
put a copy in a self-addressed envelope (I am in Canada; I have no
idea what are the Canad Post rates for large envelopes with 22 pages
of that part of my proposal. You do not need my resume and intro to
NN).
--
========================================================================
Andrzej Bieszczad                     | Phone:  (613) 763-2259
Bell-Northern Research, Ltd.          | Fax:    (613) 763-3283
Dept. 9K12, P.O. Box 3511, Station C, | USENET: uunet!bnrgate!eh!andrzej
Ottawa, Ontario, Canada, K1Y 4H7      | BitNet: andrzej@BNR.CA
========================================================================

Newsgroups: comp.ai.neural-nets
From: arms@cs.UAlberta.CA (Bill Armstrong)
Subject: Re: NN process emulator
Organization: University of Alberta, Edmonton, Canada
Date: Mon, 7 Sep 1992 15:46:39 GMT

kimhock%Solomon.Technet.sg (Teo Kim Hock) writes:

>We have been working on using the feed-forward NN to emulate the
>dynamics of chemical processes.  It works well for simple processes but
>fail to learn the process with non-linear time-delay, for example

>  y(t) = u(t+10-int(y(t-1)/.1)*0.2 + y(t-1)*0.8
>where 0<y(t)<1.0.

Are you missing a parenthesis ^ here? I assume so.

>This process has time-delay that is a function of the output.  We have
>tried using feed-forward net to learn the dynamics of this process
>without any sucess.  Can anyone give us some advise on this.  Thank you
>in advance.

There shouldnt be any problem in learning this function, provided you train
a feedforward net that has as inputs

y(t-1), u(t+1), ..., u(t+10)

assuming that the u-values can be predicted.  If you meant the prior
u-values, then you would have to have

>  y(t) = u(t-10+int(y(t-1)/.1))*0.2 + y(t-1)*0.8
>where 0<y(t)<1.0

and use 

y(t-1), u(t-1), ..., u(t-10).

I hope this helps, and apologize if I've not changed your problem
correctly.

If the dimensionality of the input is too high for your chosen learning
system, try ALNs.  If the ALNs do the learning OK, then I can tell you
what additional steps to take to assure safety.

To train the ALN, just run your system and produce 11-tuples, where
each one is of the form

y(t-1), u(t-1), ..., u(t-10), y(t).

Depending on what the (known) function u is like, you may have to
start simulations at quite a few values of y(0) to get enough training
data.  Embed these into an lf program with codings like 12:1 for u's
and 20:3 for the y's, and, say 100 quantization levels each.  You
might find that some of your inputs could be removed if u is simple
enough.

You can get the software for ALNs from menaik.cs.ualberta.ca
[129.128.4.241] in pub/atre27.exe for Windows 3.x and pub/atree2.tar.Z for
Unix.  I suggest you look at the Windows version in any case, as it
has a lot of documentation and examples, and the Unix version is
unadorned (and hence very portable).

Good luck.

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
From: holger@cogsci.ed.ac.uk (Holger Hank)
Newsgroups: comp.ai.neural-nets
Subject: Artifificial Noses
Date: 11 Sep 92 10:51:13 GMT
Organization: Centre for Cognitive Science, Edinburgh, UK

A summary of responses to a (not so) recent question about general
info on artificial noses has been posted to **comp.ai**

Holger
From: demers@cs.ucsd.edu (David DeMers)
Newsgroups: comp.ai.neural-nets
Subject: Re: looking for a reference
Keywords: back propagation, training, generalisation
Date: 7 Sep 92 20:24:16 GMT
Organization: =CSE Dept., U.C. San Diego

In article <18fc9uINNk5k@frigate.doc.ic.ac.uk> dd@doc.ic.ac.uk (Demetrius Dracopoulos) writes:

>I'm looking for the complete reference of :

>Jordan,M.I and Rumelhart D.E. "Supervised learning with a distal teacher".
>Anyone who knows where the above was published?

"Forward models: Supervised learning with a distal teacher",
Cognitive Science 16, 307--354 (1992).

This is the current issue, I think.

-- 
Dave DeMers			 ddemers@UCSD   demers@cs.ucsd.edu
Computer Science & Engineering	C-014		demers%cs@ucsd.bitnet
UC San Diego					...!ucsd!cs!demers
La Jolla, CA 92093-0114	(619) 534-0688, or -8187, FAX: (619) 534-7029
Newsgroups: comp.ai.neural-nets
From: ambati@acsu.buffalo.edu (Balamural K. Ambati)
Newsgroups: comp.ai.neural-nets
Subject: A couple of questions
Date: 11 Sep 92 15:26:01 GMT
Organization: UB

A couple of questions.  Are neurons (biological) analog devices or
ultimately finite machines at some quantal level?  Specifically, if they
were analog machines then is not the amount of information they can
store, process, etc. essentially infinite?  Is there any way to describe
the computational ability/capacity of a group of N neurons (I mean can
it be bounded, say less than e^(kN), for some constant k)?

There seems to be a lot of work being done with chaos and neural networks.
Is chaos theory generally accepted by neuroscientists as being able to
possibly describe network behavior?  I have read that in epileptic conditions,
the Hausdorff (and also correlation) dimension of the behavior of the
neuronal population is quite small compared to instances in which not all
the neurons are firing.  Does this mean, as might be intuitive, that during
epilepsy, the neurons are doing very little computation?

Your thoughts are appreciated.
Balamurali K. Ambati
ambati@ubunix.buffalo.edu
From: ecm@casbah.acns.nwu.edu (Edward Malthouse)
Newsgroups: comp.ai.neural-nets
Subject: Cybenko's Paper
Date: 8 Sep 92 15:11:42 GMT
Article-I.D.: news.1992Sep8.151142.21643
Organization: Northwestern University, Evanston Illinois.

I have read Cybenko's paper "Approximation by Superpoitions of a Sigmoidal
Function" appearing in Math. Control Signals Systems, 2: 303-314 (1989) and
have seen it referenced in several other papers.  Cybenko's paper shows that
a 3-layer, feedforward neural network with a hidden layer with sigmoidal
activation functions and an output layer with linear activation functions can
uniformly approximate any continuous function which maps from R^n into R^1
with support in the unit hypercube.  Cybenko defines a sigmoidal function as
a function which has a limit of one as x approaches infinity and zero as x
approaches negative infinity.   I have read that certain feedforward networks
which do not fit this form can often produce better training results.  The
deviations from Cybenko's result are as follows:

- Include direct connections between input and output layers.

- Use nonlinear activation functions on output nodes.

- Use the hyperbolic tangent function, which maps into [-1,1], as activation
  function.

- Include more than one output node so that mappings between R^p and R^q, 
  q>1, can be approximated.

Has there been any work since Cybenko generalizing the result and showing 
that networks with these modifications can also uniformly approximate an
arbitrary continuous function?  

Please e-mail responses and I will summarize if there is interest.

Thank you in advance for you help.

Ed Malthouse
Department of Statistics
Northestern University
From: ps@tangtown.hanse.de (Peter Schwarz)
Newsgroups: comp.ai.neural-nets
Subject: summary: neural net chips
Distribution: world
Date: Mon, 14 Sep 92 17:34:42 GMT
Organization: CeCa

Here is the summary of what i got, when i asked
for ANN chips.

There seems to be a group of chips called ETANN which
is built by Intel. As far as i can say these chips 
use analog and/or digital inputs/outputs. One cycle seems to
take 5us or 0.5us, depending on the chip used.


There also is a list of neural network chip manufacturers
listed in the FAQ. Search for part -A17). Neural Network Hardware.
This list arrived just one day after i posted my request.

I also received a message by Ben Bryant, that there has been
a report about anlog neurons (built by Cal Tech) in the
September 1992 issue of the Popular Science Magazine.
I have not been able to read this in Germany, but i will try to get
this article.

Further more, i received a mail by Harry Langenbacher, who writes:
> While our chips are not commercially available, we have collaborated with
> other researchers in the use of our chips, and entertain inquiries from
> groups engaged in neural-net research.

>        We currently work with (analog, digital, and optical) neural net
> and concurrent processing devices and hardware systems. We build special-
> purpose and general-purpose analog, digital, mixed-signal, and
> opto-electronic chips. For over 7 years we have been a leader in hardware
> neural nets .  We make "synapse" and "neuron" chips, opto-electronic
> chips, minimum-cost-path-finding chips, neuron/synapse chips, etc.

> Harry Langenbacher 818-354-9513 harry%neuron6@jpl-mil.jpl.nasa.gov
> Concurrent Processing Devices Group, FAX 818-393-4540
> JPL,  M/S 302-231, 4800 Oak Grove Dr, Pasadena CA 91109        USA


If i wrote something wrong please tell me. It is possible that 
some international news and emails did not reach me because we
had some trouble in Hamburg this week...

If you find an interesting article or anything that concerns available
NN chips i would be happy to get a mail by you.

Thanks in advance
                   Peter

--


+-------------------------------------------------------+
|   Peter Schwarz        | Tel.: 49-4109-6420           |
|   ps@tangtown.hanse.de | Adr.: Claudiusstr. 22        |
|                        |       D-2000 Tangstedt       |
+------------------------+------------------------------+
|   I am still looking for a HP-DJ driver for OS/2 2.0  |
+-------------------------------------------------------+
Newsgroups: comp.ai.neural-nets
From: jjb@sequent.com (Jeff Berkowitz)
Subject: Summary of CUPS + new question
Keywords: backprop implementation errors (?)
Organization: Sequent Computer Systems, Inc.
Date: Wed, 9 Sep 92 06:18:03 GMT

Some weeks back I posted a request for real examples of the performance
of back propogation simulators in "backprops/second."  Bill Armstrong
at the University of Alberta, Canada pointed out in a posting that the
accepted unit of measurement was CUPS.  He also pointed me at ALNs for
use when performance was important.

However, I received no replies stating real-world performance.

Now, the question.  While I was trying to debug my backprop simulator,
my wife discovered what appears to be a subtle difference between the
precise description of backprop and several of the "C" implementations
I've picked up via ftp.

In the "original" paper (Rumelhart, Hinton, Williams, "Learning
Internal Representations by Error Propogation", 1986) the backward
pass is described as follows:

	...The first step is to compute delta for each of
	the output units. [...] We can then compute the
	weight changes for all connections that feed into
	the final layer.  AFTER this is done, then compute
	deltas for all units in the penultimate layer...[etc,
	emphasis mine.]

Dayhoff, "Neural Network Architectures", p 67, is even more specific:

	Delta values are computed for the output layer...
	its incoming weights are adjusted...delta values
	are computed for the hidden layer.

I have found two implementations, however, in which the inner loops
of the backprop do something like this:

	compute deltas for output layer;
	for layer := prev(outout) downto input
	    for each element in layer
		for each outgoing arc from element
		    (1) accumulate output * weight of arc
			into this element's "error"
		    (2) adjust weight of arc.
		done
		use "error" to compute this node's delta.
	    done -- elements in layer
	done -- layer

It appears to me that (1) and (2) are reversed in this pseudocode;
as I read the description, I should change the weight FIRST as I back
up, and then use the NEW value in the accumulated error.   At least
the Dayhoff description pretty much states this in black and white.

Either I'm confused, or this is a very common implementation error.
If it's an implementation error, can anybody suggest what its effect
is?  I found this in programs that appear to have been used quite a
bit.
-- 
Jeff Berkowitz, Sequent Computer Systems   jjb@sequent.com  uunet!sequent!jjb
From: u894776@bruny.cc.utas.edu.au (James McCoull)
Newsgroups: comp.ai.neural-nets
Subject: Wanted: NN + Image Processing - references.
Date: 15 Sep 92 01:29:16 GMT
Organization: University of Tasmania, Australia.

Request for Information:

	I was wondering if any of you kind folks out there could
point me in the direction of information on the union of
Neural Network techniques and Digital Image Processing.  My
principle interest is not in the traditional segmentation/recognition
applications - but more in the application of Neural Networks to
tasks such as: image smoothing and filtering.

	Anyway thankyou for your time - all information and 'me toos'
to u894776@bruny.cc.utas.edu.au
From: irh@leo.bby.oz.au (Ian R Heddle)
Newsgroups: comp.ai.neural-nets
Subject: cerebellum networks
Date: 7 Sep 92 07:20:29 GMT
Organization: Burdett, Buckeridge & Young, Melbourne, Australia


Does anyone out there heard of cerebellum networks? Apparently these
networks were developed at the University of New Hampshire by a Dr
Aldus. Any hint's or clues as to these networks would be appreciated.
Regards,
Ian Heddle.
--
________________________________
  Ian Heddle
  Burdett, Buckeridge and Young
  Melbourne, Australia
  Internet irh@melba.bby.oz.au
From: dd@doc.ic.ac.uk (Dimitri Dracopoulos)
Newsgroups: comp.ai.neural-nets
Subject: Flight and NNs
Date: 9 Sep 92 14:29:50 GMT
Organization: Department Of Computing, Imperial College, London UK
NNTP-Posting-Host: swan.doc.ic.ac.uk

Hi all,

I'm looking for references of any applications of NNs to Aeronautics
(including companies that are doing research in this area).

In the end I'll summarize.

Dimitri

-------------------------------------------------------
Don't mess with Texas
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Newsgroups: comp.ai.neural-nets