HDTenEightyP/NetNews huggingface dataset:
From: d8linha@dtek.chalmers.se (Haakan Lindh)
Newsgroups: comp.ai.neural-nets
Subject: Training Data to Aluminium Appl.
Date: 26 Jul 92 21:21:21 GMT
Organization: Chalmers University of Technology

Hi,
My friend and I are currently doing our thesis project for an Aluminium
Smelter Factory in Sweden. The task is to control the owen temperature 
better than they are doing at the moment. We've got complete access to
their process database and they've explained some basics about the process.
There is, for instance, a well-known connection between the amount of added
Aluminum Flouride, AlF3 (a chemical they add once every day), and the 
temperature in the owen some time period later. Then we have all the noice
(as usual).
Our only control parameter is the amount AlF3 added each day, so the task is
to predict how much to add to receive some predefined temperature the following
day.
We're running Stuttgarts Neural Network Simulator (SNNS) under UNIX on a 
SPARCstation.

The net contains (right now) 10 inputs and then 4 binary coded output 
units (0-16). We have approx. 400 connections in the net.
We are currently trying to predict the temperature for tomorrow using super-
vised learning. It is sufficient if the predicted temperature lies within some
specified boundaries, i.e no demand of absolute correct prediction.

Our first question is about the process data:

 The process data contains a lot of data close to the temperature average and
 less and less data the futher you get from the average (like a normal 
 distributed curve). Should we remove some of the data near the average in 
 order to get a more even `spread' in the training set or should we just train
 the net with the whole set as it is?

The 2nd and 3rd quiz is regarding the training data:

 According to a prof. in Information Theory, we should uncorrelate all data
 by using the Gramm-Schmidt method. Is it common praxis to do that?

 The database contains over 25,000 records. Should we try to train the net with
 as much data as possible or is a small subset sufficient?

4th:

 We've reached quite satisfactory results by only using temperature and
 the amount of added AlF3 five days back as input parameters. We took process
 data for some months back which gave us approx. 1000 training data.
 The net reached a pretty satisfactory level after only 200 training cycles.
 The temperature depends on other parameters as well, but their influence is
 not as great as AlF3. Should we add them as well to get even better results
 and, in that case, should we add as many as we can or is it better to add 
 only a few to keep the net as small as possible?

We would really appreciate any response to the question above and especially
if someone knows about any similar project (ongoing or completed).


--
d8linha@dtek.chalmers.se
                                  Chalmers Univerity of Technology, Sweden
                               Department of Computer Science and Engineering
From: rpatil@nmsu.edu (Raj Patil)
Newsgroups: comp.ai.neural-nets
Subject: Object Classification...
Date: 27 Jul 92 20:58:18 GMT
Distribution: comp.ai.neural.nets
Organization: Computing Research Lab


Hi:

I am faced with following problem to be solved using image processing
techniques:

We are trying to classify different non-lint particles in a cotton sample
using imaging techniques.

Cotton sample after ginning contains different kind of trash material on the
basic of which cotton is graded for it quality. The trash (non-lint particles)
are of type pieces of leaf, bark, sticks, pepper (fine pieces of leaf), 
cotton seed coat fragments, etc. We are looking at different features
of these particles, area, perimeter, shape factor, eccentricity, moments etc.
Using these features we have achieved fairly good classification. We also 
used clustering approaches, Neural nets etc. 

Those who have seen some of these non-lint particles, is these anything else
that we need to consider as feature, in order to get a better 
classification ? Like most of real life problems, one major problem is 
of overlapping clusters of these trash types and increasing feature 
space may improve the classification.

Any suggestions and ideas are appreciated.

Regards

raj
USDA, ARS
From: doherty@vax.oxford.ac.uk
Newsgroups: comp.ai.neural-nets
Subject: Intl Workshop on VLSI for NN & AI
Date: 23 Jul 92 11:10:59 GMT
Organization: Oxford University VAXcluster

3rd International workshop on VLSI for Neural Networks and Artificial
                           Intelligence

			1-4 September 1992

at Jesus College, University of Oxford, England.

Organised by the Departments of Continuing Education and Engineering Science
with the Department of Electrical Engineering, SUNY-Binghamton.

For details and registration form, please contact Maureen Doherty at the
Department for Continuing Education on fax: +44 (0)865 270284. Or e-mail.
Please supply full address.
From: sl859@cc.usu.edu
Newsgroups: comp.ai.neural-nets
Subject: Thanks Neural Nets, and Brains?
Date: 21 Jul 92 16:20:33 MDT
Organization: Utah State University

Hey Neural Netters,

	I posted a message a while back and wanted to thank everyone
for their replies.  They were very helpful, although we have a long 
way from being done, but we have a good idea where to go with much 
of the suggestions given as replies.  I also have a better understanding
of the fact that you should not understand much of the Backpropagation 
workings, and am becoming very familiar with data tweaking and data
conflaggellation, but that's one of the joys of Neural Nets :).
 	I do have an additional question.  How extensive has the research
been on neurons of a neural net and the actual neurons of the brain?  I 
am interested mostly out of curiosity, but from what I've read I can only 
deduct that we still haven't got a very good model of the actual workings 
of the brain neurons, let alone how to model them with computers.  I can
imagine a reason would be that people who get involved in computers AI 
don't have the biological experience (or patience to get both degrees!)
to know the inner most workings of these cells and viceversa.  One book
I read however claimed even the best NN's simulated by computers barely
reach the intellectual capacity of a tapeworm!  (I liked that quote!)
	Well, thanks to all those who responded some time ago and thanks
for reading this other question.  I find much of this material very 
fascinating, and apreciate the pointers posted for the beginners as well.
				Keep trainin' and testin' all those Neurons!
					wReam...
					(Raymond Bingham)
				Internet:SL859@cc.usu.edu

Xref: sparky comp.ai.neural-nets:2890 comp.ai:2881
From: erwin@trwacs.fp.trw.com (Harry Erwin)
Newsgroups: comp.ai.neural-nets
Subject: Training Networks on Chaotic Time Series
Keywords: chaos backpropagation
Date: 23 Jul 92 11:21:46 GMT
Organization: TRW Systems Division, Fairfax VA

I've noticed a lot of interest over the last year in using neural networks
to predict chaotic time series. I'd like to remind people of a result
posted here about a year ago. During the summer of 1991, my son, Jeremy
Erwin, conducted a parametric study under my direction where he trained a
standard backpropagation network on chaotic time series to see whether the
network was learning the training sets or generalizing. The chaotic time
series were generated using the logistic map with the multiplicative
coefficient parametrically varied over the chaotic region. He discovered
that the network was clearly learning the training set and not
generalizing. This was particularly clear for the more highly chaotic
cases since the effectiveness of the network was significantly reduced for
a fixed training period. It was also clear that the network "hadn't a
clue" for regions of the chaotic process that were not represented in the
training set.

Cave canem,
-- 
Harry Erwin
Internet: erwin@trwacs.fp.trw.com

Newsgroups: comp.ai.neural-nets
From: bdbryan@eng.clemson.edu (Ben Bryant)
Subject: Re: Thanks Neural Nets, and Brains?
Reply-To: bdbryan@eng.clemson.edu
Organization: College of Engineering, Clemson Univ.
Date: Thu, 23 Jul 1992 01:37:55 GMT

In article 57397@cc.usu.edu, sl859@cc.usu.edu () writes:
>Hey Neural Netters,
>
>	I posted a message a while back and wanted to thank everyone
>for their replies.  They were very helpful, although we have a long 
>way from being done, but we have a good idea where to go with much 
>of the suggestions given as replies.  I also have a better understanding
>of the fact that you should not understand much of the Backpropagation 
>workings, and am becoming very familiar with data tweaking and data
>conflaggellation, but that's one of the joys of Neural Nets :).
> 	I do have an additional question.  How extensive has the research
>been on neurons of a neural net and the actual neurons of the brain?  I 
>am interested mostly out of curiosity, but from what I've read I can only 
>deduct that we still haven't got a very good model of the actual workings 
>of the brain neurons, let alone how to model them with computers.  I can
>imagine a reason would be that people who get involved in computers AI 
>don't have the biological experience (or patience to get both degrees!)
>to know the inner most workings of these cells and viceversa.  One book
>I read however claimed even the best NN's simulated by computers barely
>reach the intellectual capacity of a tapeworm!  (I liked that quote!)
>	Well, thanks to all those who responded some time ago and thanks
>for reading this other question.  I find much of this material very 
>fascinating, and apreciate the pointers posted for the beginners as well.
>				Keep trainin' and testin' all those Neurons!
>					wReam...
>					(Raymond Bingham)
>				Internet:SL859@cc.usu.edu
>

I read an article recently in of all places Popular Science magazine that
spoke of the "Teraflops race" among major computer vendors. In the article,
there were a few scientists mentioned who wanted to "milk" the power of those
machines to more closely model the actual brain's function. It was really
quite intriguing. The one scientist (I can't remember his name) said that
he would one day like to be able to follow the path of firing responses through
the brain using some MAJOR HEAVY DUTY processing and some HYPER-SENSITIVE
electrical sensors. -I believe when that time comes, this the field of
artificial life (Data from Star Trek?) will appear. That was an article in
one of the spring `92 issues of PS.

"Who are you!!!!!" He screamed in Holy Terror.
"I'm Robo-man"

-Benjamin Bryant
<bdbryan@eng.clemson.edu>
From: prechelt@i41s14.ira.uka.de (Lutz Prechelt)
Newsgroups: comp.ai.neural-nets
Subject: any complicated units/connections used ?
Date: 27 Jul 1992 12:46:18 GMT
Organization: University of Karlsruhe, FRG
NNTP-Posting-Host: i41s14.ira.uka.de

Hello netters,

in almost all neural network models used today, the connections are 
restricted to multipying their input with a weight and the units 
are restricted to applying a mapping function (e.g. threshold or 
squashing function) to their cumulated inputs.

I would like to know about all *more complicated* forms of units or
connections anybody is using or planning to use in a neural network.
I am aware of sigma-pi units, activation decay, and time delay 
connections but please send me info about how you use these, anyway.

Please reply by email, I'll 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: inde47d@jane.uh.edu (Shivakumar, Vaithyanathan)
Subject: Application Specific Integrated Circuits  ????
News-Software: VAX/VMS VNEWS 1.41    
Organization: University of Houston
Date: Thu, 30 Jul 1992 05:49:00 GMT

I was recently told about the existence of ASICS (Application Specific 
Integrated Circuits). The person who told me about this could tell no more
than just the existence of such chips. I was wondering whether somebody
could shed some more light on these. I am particularly interested in 
knowing what algorithms have been implemented in ASICS. Also, are there 
any ASICS for any combinatorial optimization problems ????

I will summarize if there is enough interest.

Thank you 

Shiv
Dept. Of Industrial Engineering
University of Houston
From: bill@nsma.arizona.edu (Bill Skaggs)
Newsgroups: comp.ai.neural-nets
Subject: Re: Neural Nets and Brains
Date: 25 Jul 92 03:11:36 GMT
	<arms.711986585@spedden> <1992Jul24.235828.23813@u.washington.edu>
Organization: ARL Division of Neural Systems, Memory and Aging, University of
	Arizona

davisd@milton.u.washington.edu (Daniel Davis) writes:

   >I personally do not believe that viewing neuron function in a
   >binary fashion will be fruitful until one models the sequencing
   >and rate of firing of action potentials.

It has already been fruitful.  Virtually all of our current
understanding of neural mechanisms of associative memory comes from
binary models:  Anderson's model, Willshaw's model, Hopfield's model,
the Boltzmann machine, Kanerva's Sparse Distributed Memory, etc.

We now face the problem of linking these abstract models with the gory
reality of the brain, but in the absence of such models, we would
be entirely at sea without a compass.

   > As for the non-applicability of continuous functions to neuron
   >functioning, I would suggest that continuous valued artificial
   >neurons can be viewed as modelling the rate at which different
   >neurons fire action potentials. (The rate of fire is often what
   >conveys information, not simply whether or not a neuron has
   >fired.) 

Exercise:  Give a precise, mathematical definition of the "rate of
fire" of a neuron.  It's a lot harder than it looks!  In fact, as far
as I can tell, there *is* no perfectly good definition (though there
are ones good enough to be useful).

	-- Bill
From: erwin@trwacs.fp.trw.com (Harry Erwin)
Newsgroups: comp.ai.neural-nets
Subject: Re: Thanks Neural Nets, and Brains?
Date: 26 Jul 92 13:27:54 GMT
Organization: TRW Systems Division, Fairfax VA

>I also believe work on chaos in man-made neural nets may also be a
>useful tool for understanding chaos in real neural systems.

Why?

(What you're essentially saying is that the non-linearity of man-made
neural nets is similar to the non-linearity of real neural systems.)

Cheers,
-- 
Harry Erwin
Internet: erwin@trwacs.fp.trw.com
From: blackman@hodgkin.med.upenn.edu (David Blackman)
Newsgroups: comp.ai.neural-nets
Subject: Re: Thanks Neural Nets, and Brains?
Date: 24 Jul 92 16:52:44 GMT
Organization: University of Pennsylvania

In article <arms.711932483@spedden> arms@cs.UAlberta.CA (Bill Armstrong) writes:
>First off, isn't it rather strange that the most widespread artificial
>model of neural operation. the multilayer perceptron, uses continuous
>quantities on its connections, while the dendrites and axons of
>neurons use "zero or one" type action potentials?

This doesn't take into account the fact the once an action potential
reaches a synapse, it generates a postsynaptic EPSP.  The capacitance
of the membrane allows for analog summing of the the EPSP's and therefore
gives an analog or continuous valued voltage at the postsynaptic
membrane. 
-- 
David Blackman
blackman@hodgkin.med.upenn.edu
(215) 898-3553

Newsgroups: comp.ai.neural-nets
From: tedwards@src.umd.edu (Thomas Grant Edwards)
Subject: Re: neural nets and generalization (was Why not trees?)
Organization: Systems Research Center, Maryversity of Uniland, College Park
Date: Fri, 24 Jul 1992 19:51:38 GMT

In article <arms.711990060@spedden> arms@cs.UAlberta.CA (Bill Armstrong) writes:
>paulking@next.com (Paul King) writes:
>I agree that discovery of subfeatures is useful, but how much extra
>interconnection is required -- complete connections to all elements in
>the preceding layer?  If you cut down on the number of connections, you
>cut the time of executing the net.  Go too far -- and you eliminate the
>possibility of finding subfeatures that are shared.

I'll put forth that finding sub-problems in neural systems is not just
useful, but required for any significant real-world problem.
Juergen Schmidhueber said it best, when he talked about real-time
recurrent learning of a robot to find it's way home from university.
It could either learn every little microscopic detail of how to get
from place to place, which would be a moot point anyway since learning
algorithms would fail with that much which has to be learned, or it could
break the trip down into important sub-goals such as "open the office door"
which it could later re-use for opening up any kind of door, "walking
from bldg A to home" "opening up front door," which would again use the
"open door" sub-goal it already learned, etc.

The point is, machine-learning people and traditional AI people understand
the need for knowledge organization, and it is real, and we can't get
around it in connectionism by just throwing more hidden units at the
problem.  We have to begin to cast knowledge organization into a
connectionist light.  Cascade-Correlation is a first-order example of
trying to break down goals into sub-goals, but I think we can expand on
error-correlation as an organizational metric.

-Thomas Edwards

Newsgroups: comp.ai.neural-nets
From: tom@cs.su.oz.au (Thomas James Jones)
Subject: Re: re:need for unique test sets
Reply-To: tom@cs.su.oz.au
Organization: Basser Department of Computer Science
Date: Mon, 27 Jul 1992 10:14:01 GMT

In article <1992Jul22.031319.15531@afterlife.ncsc.mil>, hcbarth@afterlife.ncsc.mil (Bart Bartholomew) writes:

|> 
|> 	Let me try again.
|> 	If you have trained the net on the training set
|> and ithe net gets all the answers right according to some
|> arbitrary measure (not necessarily MSE) AND if your
|> test set contains some of the same input/output pairs as
|> are in the training set, then the net will always get those
|> right, and will cause the apparent success on the test set
|> to look better than it really is.  Unless, of course, it
|> gets all the test set right, and then the point is probaly moot.
|> 	The point of having a test set (taken from the same 
|> source as the training set) is to make sure the net has found 
|> the right (or equivalent) function.
|> 	On the other hand, if the net does well on the training
|> set but falls apart (scores badly) on the test set, you know that
|> the net has found a nice function that describes the training 
|> set well, but *is not the function that actually generated the 
|> data*.  In that case, the net is worthless. 

	I guess this is pretty difficult to answer, but what
	basis do we have for expecting that the net should
	(or can) learn a function we have 'in mind', when there
	are infinite trivially different ways of mapping any
	given data set?
	If we have no organizational basis for our learning, then
	what inherent organizational concepts do we expect the
	net to extract?

	tom

Xref: sparky comp.ai.neural-nets:2994 sci.image.processing:244
From: rajapaks@acsu.buffalo.edu (Jagath Rajapakse)
Newsgroups: comp.ai.neural-nets,sci.image.processing
Subject: Images Needed
Date: 28 Jul 92 13:53:20 GMT
Organization: UB


 Hello everybody,
  
   I am looking for the following images for some experiments relating to 
   my thesis.

    (1). Pentagon Building Image.
    (2). An intensity image and A range image taken from the same
         scene (registered).
	
   If anyone willing to provide me with the above image/s, could email
   me at rajapaks@eng.buffalo.edu .
  
  thanks for your kind consideration.

  -Jagath Rajapakse
   Dept of Electrical Engineering.
   SUNY  at Buffalo.

  
Newsgroups: comp.ai.neural-nets
From: tsioutsias-dimitris@CS.YALE.EDU (Dimitris Tsioutsias)
Subject: What can NN really do.
Organization: Yale University Computer Science Dept., New Haven, CT 06520-2158
Date: Thu, 23 Jul 1992 02:41:36 GMT

I've been reading for some time the postings in this newsgroup and 
I have the feeling that many people have turned to the use or 
had to turn to the use of NN without having realized what actually
can and what cannot be done with them. Many think that if you set up
a bp net, and feed it with some data, then you have solved all your
problems!

In fact, NN are just a test bed for the much more complicated
and comlpex (and thus evasive, along with its rules) prototype of
knowledge aquisition, that is the brain. As with powerful supercomputers
that run simulations to test the evolution of the Universe based on
well established known physical laws but on a much smaller and simplified
scale, the NN are just the tools to emulate the behaviour of the living
tissue that forms up the brain. 

The point to understand here is that what is important is not so much the 
use of 10 or 100 units here or there, but the unraveling of the RULES that
are hidden behind them. HOW these neurons interact with each other in such
a massively parallel and compicated way, as happens with the brain itself.

So, try to experiment with the rules and the dynamics of the nets and
not merely with a good or better data set. After all, the human brain
can easily generalize and be trained with poor training sets and even
learn more from "false" data, that enable it to extrapolate, analyze and
LEARN. Don't loose the forest behind the first few trees!


->dimitris
From: connolly@rabbit.cs.umass.edu (Christopher Ian Connolly)
Newsgroups: comp.ai.neural-nets
Subject: Re: Neural Nets and Brains
Date: 27 Jul 92 21:20:17 GMT
Organization: University of Massachusetts, Amherst

In article <BILL.92Jul23224539@ca3.nsma.arizona.edu> bill@nsma.arizona.edu (Bill Skaggs) writes:
>Well, McCulloch and Pitts proved a long time ago that their networks
>could emulate any Turing machine; it's hard to get any any stronger
>than that.

I know this is a nit as far as this thread goes, but I think it should
be clarified:

I don't recall McCulloch and Pitts proving Turing equivalence (this
doesn't seem possible without a potentially infinite supply of units),
but as I recall, Kleene proved that McCulloch-Pitts networks were
essentially equivalent to finite state automata.  If I remember
correctly, this shows up in "Automata Studies", Shannon and McCarthy,
eds., Princeton University Press, 1956.

Is this what you were thinking of?
-- 
	-	-	-	-	-	-	-
Christopher Ian Connolly			connolly@cs.umass.edu
Laboratory for Perceptual Robotics		wa2ifi
University of Massachusetts at Amherst		Amherst, MA 01003
Newsgroups: comp.ai.neural-nets
From: sareibi@cheetah.vlsi.waterloo.edu (Shawki Areibi)
Subject: Software for Neural Nets in Combinatorial optimization
Organization: University of Waterloo
Date: Sat, 25 Jul 1992 22:41:17 GMT

hi, I was wondering if you could give me some information on software available
in C language for Neural nets that solves "combinatorial optimization problems"
i.e Competition based neural nets or hopfield nets

Newsgroups: comp.ai.neural-nets