HDTenEightyP/NetNews huggingface dataset:
From: PADay@cen.ex.ac.uk
Newsgroups: comp.ai.neural-nets
Subject: anyone compiled xerion on Indigo?
Date: 15 Oct 92 10:29:59 GMT
Organization: University of Exeter, UK.


Hi, has anyone compiled xerion on an Indigo?

What did you do to the makefiles?

Any assistance appreciated as my compile always blows up by trying to create
the src/lib/include/xerion directory more than once, with the result that
the files that should be in there are not.

Paul
From: orsier@imag.imag.fr (Bruno Orsier )
Newsgroups: comp.ai.neural-nets
Subject: Request : ref of the connectionist scientist game
Date: 12 Oct 92 19:34:04 GMT
Organization: Institut Imag, Grenoble, France

I have an urgent need of the exact reference of an article that I've ftp'ed
from Neuroprose :
"The Connectionist Scientist Game : Rule extraction and Refinement in a Neural
Network"
C. McMillan, M. C. Mozer, P. Smolensky.

Thanks a lot by advance,
Bruno Orsier.

-- 
----------------------------------------------------------------------------
Bruno ORSIER | (33) 76-57-48-08	| orsier@lifia.imag.fr | fax : (33) 76574602
           projet SHERPA "Dynamique des bases de connaissances"
    LIFIA-IRIMAG, 46 Avenue Felix Viallet, 38031 GRENOBLE Cedex, FRANCE 
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Newsgroups: comp.ai.neural-nets
From: ling@csd.uwo.ca (Charles X. Ling)
Subject: "Difficult" functions of ANN
Organization: Department of Computer Science, University of Western Ontario, London
Date: Sun, 11 Oct 1992 21:53:57 GMT


Hi ANN world,

What classes of "simple" Boolean functions that are difficult to learn
by neural nets with one layer of hidden neurons? ("Simple" in the sense
that the functions are easily describable in some representation.)
In particular, which Boolean functions require an exponential number
of one-layer hidden neurons in order to compute all of the
$2^n$ tuples of the functions with n variables correctly?
Assume the output function is the threshold or the logistic function.

Thank you very much in advance.

Cheers,
Charles
Newsgroups: comp.ai.neural-nets
From: gkirikos@epas.utoronto.ca (George Kirikos)
Subject: Re: Pointers to Stock Market Databases
Organization: University of Toronto - EPAS
Date: Wed, 7 Oct 1992 19:04:12 GMT

In article <Bvr85H.8v1@netnews.jhuapl.edu> fernando@aplcomm.jhuapl.edu (Fernando Pineda) writes:
>Financial data bases are easy to get if you have the $'s. This is for the 
>obvious reason that information is a valuable resource in the markets and the 
>purveyor's of that information will charge whatever the market will bear. 
>I have three alternatives:
>
>
>
>(C) -----
>The third alternative is to know some "in the business." By far and away the
>best way to go -- But  doesn't generalize very well!
>
>good luck -- Fernando 
>
>Fernando Pineda (fernando@aplcomm.jhuapl.edu)

A fourth alternative is to be an academic, and have access to the CRSP
database (Centre for Research of Securities Prices). This data file is
licensed by many universities, although it should not be used for
commercial purposes. BTW, the data goes back to 1961, for all NYSE,
AMEX, and NASDAQ stocks, monthly and daily. Inxexes and other data
(prices, hi/lo, beta, returns, etc.) are included (although the
university may have licensed only a subset of the data).

I have CRSP's phone number and Internet address, but not where I'm
presently located. Leave me a followup message (or email me) if you
desire this information.

+---------------------------------------------------------------------------+
| George Kirikos                   Internet: gkirikos@epas.utoronto.ca      |
| Toronto, Canada                 Telephone: (416) 537-1756                 |
+---------------------------------------------------------------------------+
From: frank@proteus.qc.ca (Frank Salvatori)
Newsgroups: comp.ai.neural-nets
Subject: Re: References to financial modeling/forecasting
Keywords: financial forecasting
Date: 9 Oct 92 16:50:25 GMT
Reply-To: frank@proteus.qc.ca (Frank Salvatori)
Organization: The Proteus Group, Montreal Canada

yoshio@tuna.cis.upenn.edu (Yoshio Yamamoto) writes:

Have you checked the IJCNN for 1989, 1990, 1991 put out by the IEEE. There are
many articles on forecasting financial applications. The references in the
articles are also useful.

---
Frank Salvatori 
The Proteus Group               Internet: frank@proteus.qc.ca
Montreal, Quebec, Canada        uucp: uunet!proteus!frank
From: 86u0364@csdvax.csd.unsw.edu.au
Newsgroups: comp.ai.neural-nets
Subject: AT&T Neural Net Chip
Date: 9 Oct 92 08:53:28 +1000
Organization: University of New South Wales

I am currently trying to find some details of a neural network chip
manufactured by AT&T. I think it is called NET32K but I'm not sure
If anyone has any details, I would greatly appreciate it if they could forward
me a copy.
Thanks in advance.
Alfred Fong

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
University of New South Wales           Voice: 61 - 2 - 697 - 4556
School of Physics                       Fax:   61 - 2 - 663 - 3420
Dept. of Biophysics                     Email: 86u0364@csdvax.csd.unsw.edu.au
P.O. Box 1
Kensington. NSW. 
Australia. 2033.

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Newsgroups: comp.ai.neural-nets
From: yair@siren.arc.nasa.gov (Yair Barniv)
Subject: NIPS/ Vail room
Keywords: NIPS, Vail room
Reply-To: yair@windchime.arc.nasa.gov
Organization: NASA Ames
Date: Thu, 15 Oct 1992 20:32:16 GMT

I have a reservation at the Marriott's Mark Resort in Vail which
I must cancel (and lose $50). Arrival date is Thu Dec 3 92,
departure is Dec 8. These dates can be changed down to only a
single-night stay. Does anyone out there need to make a reservation
and doesn't mind to use mine (changing my name on that reservation
into his/her).

Please contact me at yair@windchime.arc.nasa.gov

Yair Barniv
From: shyam@suntan.ece.clarkson.edu (Shyam Kamadolli)
Subject: ANNs in Power Systems ?
Organization: Clarkson University
Date: Tue, 13 Oct 1992 21:49:53 GMT


Hi Netters,

I am looking for references on two specific topics in the above field.

1. High-impedance fault detection using Neural/AI systems

2  Neural-net based relay controllers.

Any pointers would be appreciated.

Thanks in advance.

Shyam


shyam@kaman.com 315-734-3634
--
______________________________________________________________________________
Clarkson University |           Shyam Kamadolli             | 7 Leroy Street
Potsdam    NY 13676 |                                       | Potsdam NY 13676
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Newsgroups: comp.ai.neural-nets
From: mgcbo@uxa.ecn.bgu.edu (Charles B. Owen)
Subject: Re: Noisy backprop- references needed
Organization: Educational Computing Network
Distribution: usa
Date: Thu, 8 Oct 1992 18:00:41 GMT


scotbri@rosemount.com writes:

>From article <Bvpp9F.617@epix.rrze.uni-erlangen.de>, by arras@bfws46.forwiss.uni-erlangen.de (Mike Arras):
> 
> In article <Bvoo1v.IIq@uxa.ecn.bgu.edu>, mgcbo@uxa.ecn.bgu.edu (Charles B. Owen) writes:
>> ...
>>Adding noise (and white noise is a bad choice, normal or
>>gaussian is better), to the wieghts (and sometimes just the inputs,

>Talking about white noise vs. normal or gaussian is an apples and oranges
>thing.  "White" refers to the spectral content whereas "normal or 
>gaussian" refers to the statistical distribution.  You can have gaussian
>noise that is white or "colored".

>Perhaps you meant to make the distinction between gaussian and uniform
>statistical distributions.

Yes, I am referring to gaussian and uniform distributions.  Pardon the
unusual terminology, but I have often seen "white" refering to equal or
uniform distributions.  When I refer to white, I am indicating uniform
distributions with a mean of 0.  Normal distribution is the same as
Gaussian distribution, but most newer texts that I have seem to have
dropped the Gaussian terminology in favor of "Normal" distribution.  

Charles B. Owen
Western Illinois University


Organization: Senior, Psychology, Carnegie Mellon, Pittsburgh, PA
Newsgroups: comp.ai.neural-nets
Date: Wed,  7 Oct 1992 01:03:07 -0400 
From: "Richard Earle Seeger, III" <rs6t+@andrew.cmu.edu>
Subject: RE: Stock Market Databse




Steve Bryant (bryant@neural.Kodak.COM) asks:

>Does a stock market performance data base exist for any of the 
>popular indexes? I would like to test a network against stock
>market prediction.



	I have recently been interested in the construction of stock market
predicting neural networks and, like you, have been hunting for on-line
databases of stock market data. I have discovered that this data is very
hard to come by- but it does exist. Dow has a database called "trade
line" to which you must subscribe for $2995, and pay searching charges
of $2.00 minute plus .05 for each quote. If you want to call the Dow,
the number is 609-452-1511. Another option is to find back issues of the
Wall Street Journal and enter the data by hand. 

	I was originally planning on using individual stock prices over the
past 5 or so years to predict individual stocks, but since this data is
so hard to come by, I have decided to try a more global prediction
paradigm. I intend to use the value of the dollar in all major countries
over the past five years to predict the movement of the Dow Jones
Average. This input data (the value of the dollar) is less voluminous
and could be typed in manually. Is this something similar to what you
had in mind?

Rick Seeger
Carnegie Mellon University
(rs6t@andrew.cmu.edu)
Newsgroups: comp.ai.neural-nets
From: thimm@quine.ira.uka.de (Georg Thimm)
Subject: Idea to optimize NN
Organization: Mantafahrer Anonym
Distribution: comp
Date: Tue, 13 Oct 1992 18:46:08 GMT


Hello,
I am thinking about optimization of NNs. My idea was: 

A net (for example a perceptron with n hidden layers) consists of more
neurons and synapses then required for a adaption to a special
problem. This results in a slow learning process and is expensive. 
If the input data to be learned are correlated ( like the fifth bit
always equal to the sixth bit) or restricted in space 
then this has (?!) consequences for the weights. 
Very simple examples for this consequences or consequences
resulting from the learning process may be that a weight is
approximately 0 and remains there for a couple of learning steps. Why don't skip
this synapse and continue learning? A second case would be: All
weights of a neuron of the input layer are (quite) equal. If the
input data are normalized, the output of this neuron will be the same
for every data set. It can be removed without lost if the treshold of
the following neurons are changed.

As this special cases won't occur very often, one has to find adaption
rules or transformation rules forcing this cases.

I have 3 questions about it: Is my
idea just rubbish, and if not did you hear somebody is doing the same?
Do you have references?

Georg
--




*****************************************************************
Georg Thimm, Schwarzwaldstr. 52, W-7128 Trossingen 1, Deutschland
E-Mail: thimm@ira.uka.de
From: tmw@festival.ed.ac.uk (Tom WRight)
Newsgroups: comp.ai.neural-nets
Subject: nn in statistical physics
Date: 9 Oct 92 15:10:49 GMT

In the FAQ applying nn to  stastical physics 
problems is mentioned?

Could somebody email a few introductory references

Thanks
From: niranjan@eng.cam.ac.uk (Mahesan Niranjan)
Newsgroups: comp.ai.neural-nets
Subject: Re: How to compute the inverse Hessian matrix FAST
Date: 14 Oct 92 14:17:28 GMT
Organization: Cambridge University Engineering Department, UK

In article <1992Oct13.201549.9507@daimi.aau.dk> usatoday@daimi.aau.dk (Peter Dalsgaard) writes:
>
>Subject: Re: How to compute the inverse Hessian matrix FAST
>Well... The subject line pretty much tells the story
>

Depends on "Hessian of what?" of course. If you are working with a linear
model (or models linear in parameters like radial basis functions), the
Hessian will have a Toeplitz structure, which for 5x5 looks like:
		r0 r1 r2 r3 r4
		r1 r0 r1 r2 r3
		r2 r1 r0 r1 r2
		r3 r2 r1 r0 r1
		r4 r3 r2 r1 r0
This can be inverted efficiently by Levinson recursion - see p187 of Matrix
Computations by Golub and van Loan, second edition, for the MATLAB-like
algorithm.

Now a question for you - how can I compute the determinants of all leading
submatrices of the Hessian fast (or recursively)?
i.e determinants of each of,

  r0      r0 r1     r0 r1 r2    r0 r1 r2 r3    r0 r1 r2 r3 r4
	  r1 r0     r1 r0 r1    r1 r0 r1 r2    r1 r0 r1 r2 r3
		    r2 r1 r0    r2 r1 r0 r1    r2 r1 r0 r1 r2
				r3 r2 r1 r0    r3 r2 r1 r0 r1
					       r4 r3 r2 r1 r0
thanx
niranjan
From: rickr+@CMU.EDU (Richard Dale Romero)
Newsgroups: comp.ai.neural-nets
Subject: Re: E-mail address of Michael A. Franzini
Date: 9 Oct 92 20:32:22 GMT
Organization: Applications Software, Carnegie Mellon, Pittsburgh, PA

If you send mail to maf+@cmu.edu, it should get to him,
although he's no longer at CMU, he's in Spain.

Rick Romero
Applications Software Specialist
Computing Services
Newsgroups: comp.ai.neural-nets
From: fernando@aplcomm.jhuapl.edu (Fernando Pineda)
Subject: Re: Pointers to Stock Market Databases
Reply-To: fernando@aplcomm.jhuapl.edu (Fernando Pineda)
Organization: Johns Hopkins University/Applied Physics Labs
Date: Wed, 7 Oct 1992 13:58:28 GMT

Financial data bases are easy to get if you have the $'s. This is for the 
obvious reason that information is a valuable resource in the markets and the 
purveyor's of that information will charge whatever the market will bear. 
I have three alternatives:

(A) -----
The following companies will sell you comprehensive financial time-series data:

1) Technical Tools: (415) 948-6124
2) Genesis Data Services (719) 260-6111


(B) -----
Cheap data is harder to get especially if you want a long time series. 
Short term ( <1 year) data for all stocks traded on NYSE, NASDAC and AMEX
can be obtained inexpensively from the Dow Jones News Retrieval Service.

They offer a reasonable (to you and me) fixed price ($25/month) service.
For this price you get unlimited after-hours access to a subset of their data
bases. Some of the selected databases go back for more than one year. If you
really want to go back several years then you generally have to use their 
TRADELINE data base. You have access to TRADELINE for your $25 but there are 
additional charges for log-in time and for the number of characters transmitted.
This can get very expensive, very fast ( I don't know how this compares with 
alternative (A)) . I only have their customer service number handy: (609)-452-
1511.


(C) -----
The third alternative is to know some "in the business." By far and away the
best way to go -- But  doesn't generalize very well!

good luck -- Fernando 

Fernando Pineda (fernando@aplcomm.jhuapl.edu)
From: heinzl@elroy (Heinz Lottermoser)
Newsgroups: comp.ai.neural-nets
Subject: Re: Do neurons fatigue?
Date: 12 Oct 92 22:50:56 GMT
Distribution: comp
X-Newsreader: Tin 1.1 PL4

hucka@engin.umich.edu (Michael Hucka) writes:
: Hello,
: 
: I would like to try to find out whether there is evidence for fatigue in
: neurons.  Specifically, I'm interested in finding out whether there is
: evidence for fatigue effects in non-receptor neurons in vertebrates, in
: particular in cortical cells.  By fatigue I mean a decrease in responsiveness
: of a particular cell as a result of continued stimulation, and not a process
: of habituation.
: 
: If any of you know about research that deals with this topic, please let me
: know via email.
: --
: -- Mike
: _____________________________________________________________________________
: Mike Hucka                     | Internet: hucka@engin.umich.edu    
: University of Michigan AI Lab  | 1101 Beal Ave., Ann Arbor, MI 48109-2110

-----------------------------------------------------------------------
Yes.  Lookup the electrolyte mechanics involving rapid, or repeated firing of
the neuron.  Exhaustion(reduction actually) of electrolytes is possible, thus
retarding the firing thresholds.  There is a limit to how fast the neuron
can replenish the electrolytes inside of the cell since it is requires
transport across the cell membrane.  This transport requires energy and
can time, thus you for rapid firing, or hyperstimulation of the neuron
the refresh rate drops and thus the firing threshold.
--
-----------------------------------------------------------------------
 Heinz O. W. Lottermoser Jr.    | Software Engineer, Product Development
 EMAIL:  heinzl@dsinet.uucp     | Voicelink Software Group Lead
 PHONE: (206)881-7544           | Digital Systems International, Inc.
      __o                       | 6464 185th Ave. NE
    _ \<._                      | Redmond, WA    98052-5032
   (_)/ (_)   Bike it...        | DIDPH: (206)556-8008 ext2390  
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Hackers of the world, unite!
-----------------------------------------------------------------------
From: spxmpap@thor.cf.ac.uk (Mr M P A Page 92)
Newsgroups: comp.ai.neural-nets
Subject: using NN's to classify/interpret brain scan images
Date: 8 Oct 92 10:18:22 GMT
Organization: University of Wales College of Cardiff, Cardiff, Wales, UK
X-Mailer: Cardiff Computing Maths PP Mail Open News Gateway


 I am just starting a feasibility study into the use of NN's in the
classification/interpretation of brain scan images.  The images
themselves will be generated in various ways (single photon emission,
positron emission and magnetic resonance imaging) and will consist
of 2D slices, each containing up to 256*256 pixels, each pixel having
one of up to 256 grey levels.  Each image, therefore, contains a great
deal of information and it may be necessary to preprocess before input
to a NN.  The issue of whether to use supervised/unsupervised learning
is as yet undecided, but this will depend on the results of preliminary
work.  Possible NN's include WISARD, ART(MAP), a combination of the two,
and maybe BP as a benchmark.

I would be interested to hear from anyone who has addressed problems 
similar to that above, or who has any comments to make.

Mike Page, spxmpap@thor.cardiff.ac.uk  or sgjt200@ux.sghms.lon.ac.uk
From: sef@sef-pmax.slisp.cs.cmu.edu
Newsgroups: comp.ai.neural-nets
Subject: Re: Artificial Logic Networks and CMAC
Date: 7 Oct 92 13:44:47 GMT
Article-I.D.: cs.Bvr7Iq.14z.1
Organization: School of Computer Science, Carnegie Mellon


    From: William.Stevenson@p0.f20.n7101.z5.fidonet.org (William Stevenson)

    Are there any links or similarities between Albus's Cerebellum Model Artic
    lation Controllers and Artificial Logic Networks?

There certainly is a relationship.  CMAC is a member of the neural net
family, especially since it was inspired originally by a (highly
simplified) model of what the cerebellum may be doing.

CMAC is essentially a version of N-nearest-neighbors classifier over all
the training cases, with interpolation between these points.  There is sort
a continuum of models with nearest-neighbor models at one extreme, through
radial basis functions (which cut the input space up into larger patches),
to models with a more global, wide-ranging set of basis functions, such as
the sigmoids often used in backprop.

-- 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: r80146@cs.ee.ntu.edu.tw (B.W. Jiang)
Subject: Wanted a new comp.ai.ga NEWS_group...
Keywords: Neural-Networks, Fuzzy System, GA, AI  
Organization: CS Division, Dept. of EE, Nat'l Taiwan Univ., Taiwan
Distribution: World
Date: Fri, 16 Oct 1992 12:39:44 GMT

In article <andersen.719119765@s1.elec.uq.oz.au> andersen@elec.uq.oz.au (Hans Andersen) writes:
>rvs1265@ritvax.isc.rit.edu writes:
>
>>I hail this idea and I hope this will happen in the near future.
>>I will be willing to help in this respect and will take an active part.
>
>>Thanks,
>>good Luck!!
>>Sudhakar Somayajula
>
>I also think it's about time to get comp.ai.ga
>
>Hans Andersen

    I also hope comp.ai.ga and comp.ai.fuzzy NEWS_groups will emerge. 
Neural-Networks, Fuzzy Systems and GA provide us the good research and 
application vehicles for exploring the uncertainty in the natural world.  

Thanks,


Bor-Wei Jiang  

CS Division, 
Dept. of Electrical Engineering,
National Taiwan University,
Taipei, Taiwan
From: sef@sef-pmax.slisp.cs.cmu.edu
Newsgroups: comp.ai.neural-nets
Subject: Re: SUMMARY: CRSP Stock Database
Date: 8 Oct 92 14:50:23 GMT
Organization: School of Computer Science, Carnegie Mellon


    - ** data "should be used only for research purposes by the subscribing
      institution, its faculty and students, or its employees". Anyone not
      in those categories shouldn't have access. Data can't be resold. Royalties
      need to be paid to CRSP for any non-academic or profit-making endeavor,
      and user needs to get prior written consent to do so.

I wonder if it is a commercial use to train a neural net on this data
(producing a set of weights that are related to, but that do not CONTAIN
the info of the database), and then send that neural net out into the world
to make some investments.  One (or one's high-priced lawyer) could argue
that this is the same as educational use, except in this case you're
educating a network and not a human student.  In either case, the database
owner would not have a legitimate claim on the "student's" subsequent
earnings in life.  I'm sure the database owners would see it differently,
however.

This could be an interesting court case.  It probably won't happen because
most people won't want to take unfair advantage of CRSP, and if anyone
does, it would be awfully hard to detect and prove.  The usual technique of
salting the database with a few bogus entries in obscure corners that don't
matter (but that could be detected in a derivative product) would be hard
to implement here.

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

Internet: sef+@cs.cmu.edu
From: robert@swanee.ee.uwa.edu.au (Roberto Togneri)
Newsgroups: comp.ai.neural-nets
Subject: Contact address for IEEE NN authors -- query
Date: 14 Oct 92 02:43:50 GMT
Organization: Elec Eng, Univ of Western Australia


Hi,

Referring the July 1992 issue of the IEEE transactions on Neural Networks
(Vol. 3; No. 4) and the articles titled:

"Quantifying the Neighbourhood Preservation of Self-Organizing Feature Maps"
by H.-u. Bauer and K.R. Pawelzik

We have been doing similar work with the SOM, measuring its "crinkle" (as we
call it) and attempting to parametrize speech. Our results were published
in the IEE Proceedings-I ( Vol. 138, No. 3, pp. 207-214, June 1991
and Vol. 139, No. 2., pp. 123-127 April 1992).

I would like to contact the above authors and need their current address
(postal and/or email). In fact the address of any other people involved 
with the same area of research would also be appreciated.

Thank You,
--
Dr. Roberto Togneri                        Phone: +61-9-380-2535     _--_|\
Centre for Intelligent Information Processing Systems               /      \ 
Dept. of Electrical & Electronic Engineering                        *_.--._/
The University of Western Australia        Fax:   +61-9-380-1101          v 
NEDLANDS WA 6009 Australia                 Email: robert@swanee.ee.uwa.edu.au
From: asif@sunlite.concordia.ca
Newsgroups: comp.ai.neural-nets
Subject: Hopfield Networks
Date: 15 Oct 92 03:19:27 GMT
Reply-To: asif@sunlite.concordia.ca ()
Organization: Concordia University, Montreal Quebec
Originator: asif@tiros


Hello netters:


Could anyone mail or give some help as to where I can get the algorithm for the hopfield network
I have to use it for pattern recognition of alphabets


Thanks

Asif
Newsgroups: comp.ai.neural-nets