HDTenEightyP/NetNews huggingface dataset:
From: danforth@riacs.edu (Douglas G. Danforth)
Subject: Re: Kanerva's SDM (was Re: Hopfield Nets / Pros & Cons)
Organization: RIACS, NASA Ames Research Center
Date: Sat, 7 Nov 92 01:23:55 GMT

In <1992Nov4.205034.16263@fripp.ri.cadre.com> umn@cadre.com 
(Uttam M. Narsu) writes:

>rinkus@radar.bu.edu (Gerard J. Rinkus) writes:
>: 
>: This is from an article entitled, "Comparison between Kanerva's SDM
>: and Hopfield-type Neural Networks" from Cognitive Science 12, 299-329
>: (1988)  written by James D. Keeler
>: 

>[text elided]

>: 
>: --
>: Gerard J. Rinkus
>: Dept. of Cognitive and Neural Systems
>: Boston Univ.
>: rinkus@cns.bu.edu

>I haven't heard much about Kanerva's work since I read 'Sparse Distributed Memory' many
>years ago. What's the consensus on his work? Has anyone implemented an SDM in software or
>hardware?

>--
>__________________________________________________________________
>| Uttam M. Narsu,                        | EMAIL: umn@cadre.com   |
>| c/o Cadre Technologies Inc,            | TEL:   (401) 351-5950  |
>| 222 Richmond St., Providence RI 02906  | FAX:   (401) 351-7380  |
>|________________________________________|________________________|

NASA funded a 5 year project at Ames Research Center, Moffett Field,
California to investigate applications of Sparse Distributed Memory
to Autonomous Robots, Speech and character recognition, and weather
prediction (among others).  The results of this work are published
in a number of technical reports obtainable from RIACS
(Research Institute for Advanced Computer Science) at Ames.  A
patent has been issued for a variation of the SDM design to Louis
Jaeckel of the SDM group.  Richard Prager in England implemented a
form of SDM for speech recognition.  There have been a number of
other affiliated groups.  A board level hardware implementation
of the address decoder was constructed at Stanford University under
the direction of Mike Flynn in the Electrical Engineering department.

The NASA restructuring has curtailed the SDM funding and we are all
out looking for jobs ...

-- Douglas G. Danforth

Newsgroups: comp.ai.neural-nets
From: rpatil@nmsu.edu (Raj Patil)
Subject: Intelligent Monitoring of the Environment
Organization: Computing Research Lab
Date: Thu, 5 Nov 1992 21:17:50 GMT


Hi:

I am interested in finding out any applications of Neural nets and Fuzzy
Logic towards Intelligent Monitoring of the Environment (any aspect).
I would appreciate if someone could mail me some information on any work
being done in this area or any ideas where Intelligent systems could be 
applied in this area.

Thanks in advance

Raj Patil
NMSU-CRL, USDA-ARS
Las Cruces, NM 88003
Newsgroups: comp.ai.neural-nets
From: arpw@bcars705.UUCP (Tony White)
Subject: Complex Systems Journal Information
Organization: Bell-Northern Research, Ottawa, Canada
Date: Mon, 9 Nov 1992 14:41:36 GMT

Can anyone give me the telephone number and address
of the publisher of the Complex Systems journal? 

Thank you. 

-- 
========================================================================
Tony White                            | Phone:  (613) 765-4279
Bell-Northern Research, Ltd.          | Fax:    (613) 763-3283
Dept. 9K13, P.O. Box 3511, Station C, | 
Ottawa, Ontario, Canada, K1Y 4H7      | email:  uiwhite@bnr.ca
========================================================================
Newsgroups: comp.ai.neural-nets
From: m16532@mwunix (Steven Bayer)
Subject: Re: Aspirin/Migraines
Organization: MITRE Corporation, Houston, TX
Date: Thu, 12 Nov 1992 20:47:02 GMT

Michael Baker writes:
>Some time ago I came across a copy of Aspirin/Migraines...
>Any info on this product would be greatly appreciated and 
>should probably be posted to the net to provide some 
>information to the rest of those who may be interested. Thank
>you very much in advance.

You can obtain a copy of Aspirin/Migraines from its author, Russ Leighton,
at the MITRE Corporation, McLean Virginia.  His internet address is
  russ@dash.mitre.org
He will send you instructions on how to ftp and manipulate the software.

Good luck.

Bayer
                     __o                                          
                    ~\<,                                          
___________________()/ ()______________________________________________________
Steve Bayer                                       bayer@gothamcity.jsc.nasa.gov
The MITRE Corporation        
1120 NASA Road One, Suite 600                                    (713) 333-0978
Houston, TX  77058                                          fax: (713) 333-5147
______________________________disclaimers ad nauseum___________________________ 
Newsgroups: comp.ai.neural-nets
From: block@dxlaa.cern.ch (Frank Block)
Subject: Re: NN analysis
Reply-To: block@dxlaa.cern.ch (Frank Block)
Organization: CERN, European Laboratory for Particle Physics, Geneva
Date: Fri, 13 Nov 1992 15:11:34 GMT


In article <1dvfvkINN2tv@manuel.anu.edu.au>, shuping@andosl.anu.edu.au (Shuping RAN) writes:
|> Hello,
|> 
|>     Currently I am trying to understand how a trained NN performed 
|> the given task. What is its internal funtionality, whether its internal 
|> parameters relate to the real parameters of the given problem in some
|> way.
|> 
|>     Could some one give me some ideas of how to analyse a trained NN, 
|> or some references.
|> Thank you in advance,
|> 
|> -Shuping RAN
|> 
--
First of all: you're addressing an extremely non-trivial problem with your
question. There are some ways of which I know how to approach the understanding
of what a net is in fact doing or what it has learned.

	1.) Network inversion
	Here you have an algorithm which generates input patterns for a
	previously trained network which gives a desired, predefined output.
	This seems to be a good way for finding 'essential features'. 

	J.Kindermann and A.Linden, "Inversion of Multilayer Networks"
	It appeared in Complex Systems around 1990/91. Just have a look, there
	are not too many to check.


	2.) Rule Induction using RuleNet
	Check the hidden unit's activities for correlations using statistical
	methods like Prinicipal Component and Canonical Discriminant Analysis.

	C.McMillan, M.C.Mozer and P.Smolensky, "Rule Induction through
	Integrated Symbolic and Subsymbolic Processing", to appear in 'Advances
	in Neural Information Processing Systems IV'. This one I also got
	via ftp, don't remember from where. The authors are also listening to
	this newsgroup...ask them.


	3.) Statistical analysis of hidden nodes
	By setting up a special architecture and imposing some restrictions
	to the learning process the resulting learned weights can be directly
	mapped to rules.

	S.Dennis and S. Phillips, "Analysis Tools for Neural Networks"
	I got it by ftp from I don't know where. But the authors should be
	reading this newsgroup and can tell you where.


	4.) Self organizing networks for analysis of hidden layer activations
	This is a nice idea somebody mentioned to me which hasn't been used
	yet, as far as I know. 

Hope this helps a bit. Anyway, let me know when you found the solution to
this problem :-)

Frank

===============================================================================
Frank Block
Div. PPE				BLOCKF@vxcern.cern.ch
CERN				e-mail: 
CH-1211 Geneve 23			BLOCKF@cernvm.cern.ch
Switzerland
===============================================================================
From: unregistered@newcastle.ac.uk (Graham Lamont)
Newsgroups: comp.ai.neural-nets
Subject: UK->NIPS*92 (cheap)
Summary: none
Keywords: NIPS,travel,cheap
Date: 5 Nov 92 16:49:34 GMT
Organization: Chemical & Process Engineering Dept, University of Newcastle, UK.


There is currently a 2 for the price of 1 deal (i.e you need to buy 2 tickets) to LA from Gatwick (direct + non-stop)(155 pounds return!) and I'm looking for a 2nd person who's happy to travel out on 25,26,27 November and return 6--?? December.

Getting to Denver (and back) is obviously going to mean getting an internal flight, or quite a lot of Greyhounding or Amtraking, but if you're seriously interested in doing a bit of travelling in the west USA, it looks by far the cheapest deal at the moment.

If anyone's interested please send available dates and an indication of the level of interest, and I'll get back to you asap. I've got some details about Greyhound, Amtrak, and internal flight deals.

email: Graham.Lamont@ncl.ac.uk
From: nlonginow@falcon.aamrl.wpafb.af.mil
Newsgroups: comp.ai.neural-nets
Subject: Moody and Darken: Address ?
Date: 10 Nov 92 17:30:45 GMT
Organization: USAF AL/CFH, WPAFB, Dayton, OH

Sorry folks, this is not an informative post. Skip it if you cant supply me
with the email addresses for either John Moody or Christian Darken.

Thanks,

Nick


--------------
Nick Longinow.
 Armstrong Laboratory
 Human Systems Division
 United States Air Force
Newsgroups: comp.ai.neural-nets
From: pcm@cs.brown.edu (Peter C. McCluskey)
Subject: Re: COnnectionism in Language Acquisition?
Organization: Brown Computer Science Dept.
Date: Sat, 7 Nov 1992 15:09:06 GMT

In article <1992Nov5.042806.31363@ns1.cc.lehigh.edu>, ob00@ns1.cc.lehigh.edu (OLCAY BOZ) writes:
|> Can anybody direct me to any references on Connectionism in Language
|> acquisition? Any help will be greatly appreciated..

 Jeffrey L. Elman, "Distributed Representations, Simple Recurrent Networks,
and Grammatical Structure", Machine Learning 7, p. 195 (1991). The rest of
the articles in this issue of Machine Learning are also on this subject.

 Jeffrey L. Elman, "Finding Structure in Time", Cognitive Science 14,
p. 179 (1990).

----------------------------------------------------------------------
  >> Peter McCluskey >> pcm@cs.brown.edu >>   Reunite Gondwanaland!
----------------------------------------------------------------------
From: unregistered@newcastle.ac.uk (R.G.W. Kenyon)
Newsgroups: comp.ai.neural-nets
Subject: chaos in neural networks
Keywords: chaos
Date: 12 Nov 92 12:21:20 GMT
Followup-To: nothing
Organization: Computing Laboratory, U of Newcastle upon Tyne, UK NE1 7RU.


This is my first posting to the net!

I am interested in neural networks and chaos.
I am a postgrad in computing science.
I would like to perhaps do my thesis on whether there are chaotic systems embodied in the functioning of neural networks?

This is perhapos a little general but i'm still coming up with ideas.

Is there any literature that anybody knows about on this subject?

                             .o0o.

Richard.G.W.Kenyon
dept of computing science 
newcastle university
newcastle, england, UK.

disclaimer : any opinions expressed are my own and not those of the university.

                             .o0o.

   Intoxicating at any magnification, Mandelbrot #5 is a complex scent...
   You don't need to be a theoretical mathematician ... now you can
   just smell like one.
From: dbrown@newssun.med.miami.edu (Daniel Brown)
Subject: Image Input for a Neural Net
Organization: University of Miami, School Of Medicine
Date: Thu, 12 Nov 1992 18:16:25 GMT

I'm looking for information about preprocessing for image
inputs into a neural-net.

Our project requires the use of a 480x480 24-bit color 
image file in the tiff format.  Even after converting to
a greyscale image, that's over 65,000 inputs if used
directly.  I don't know too many systems that can handle
that much information; certainly ours can't.

I've looked a couple of compression techniques: i) summing
and averaging over 100 pixels at a time (a coarse 
version of the former image), ii) coarse coding similar
to the latter), and iii) sampling of a FFT transform of
the input data.

The obvious goal is to use as much important information
without overloading the system.  Does anyone have any other
suggestions?

Dan
dbrown@newssun.med.miami.edu

Newsgroups: comp.ai.neural-nets
From: rowe@engin.umich.edu (Steven Rowe)
Subject: Do you folks want some simple source code?
Date: Sun, 08 Nov 92 14:47:05 EST
Organization: University of Michigan Engineering, Ann Arbor
Keywords: source code

Hi, netters.  I have been playing with a simple feed-forward
backprop multi-layer perceptron model on my PC clone, and I thought
that maybe some of the newbies on this group might benefit from
having a look at the code.  Rather than waste bandwidth by posting
it (not too long, but longer than this query), I thought I'd ask
how many people are interested in it (respond via e-mail, please;
don't waste comp.ai.neural-nets space with your requests).
The code is written in (supress your laughter, please) Quick BASIC,
which I chose because it comes built into DOS 5.  There are perhaps
200 lines of code.
Also, if there is a way to submit code to the ftp site for this
group, please tell me what it is.  I didn't find this in the FAQ.

                               Cheers!
-- 
Steve Rowe: B0 f- w- c g+ k+ s- e+ r-
     These folks who don't believe in evolution
are the ones who look the most like they could use some.
                                   -Frank Allison
From: tsioutsias-dimitris@cs.yale.edu (Dimitris Ioannis Tsioutsias)
Newsgroups: comp.ai.neural-nets
Subject: Neural Nets and C++
Date: 11 Nov 1992 04:59:52 -0500
Organization: Yale University Computer Science Dept., New Haven, CT 06520-2158
Distribution: world
NNTP-Posting-Host: nyaya.systemsz.cs.yale.edu

What is the opinion of those of you that have looked into Blum's
book "Neural Nets and C++" ? Is it worth buying it ? Thanks,

->dimitris
From: merelo@rana.usc.edu (Juan Julian Merelo)
Newsgroups: comp.ai.neural-nets
Subject: Re: Kohonen Algorithm used in ANSIM Neural Net Software
Date: 6 Nov 1992 12:15:23 -0800
Organization: University of Southern California, Los Angeles, CA
Distribution: world
NNTP-Posting-Host: rana.usc.edu


That was the algorithm used in the first (ie, Biol. Cyb. 1983, don't remenber the
exact quote) Kohonen papers. After that, it changed the distance to euclidean
instead of angle, and skipped normalization. I think that the only difference is
that you miss one dimension, and it may be slower, as you have to do one
additional summation.

See ya

				JJ
From: mlevin@husc8.harvard.edu (Michael Levin)
Newsgroups: comp.ai.neural-nets
Subject: need info on using neural nets to pick out visual features
Date: 6 Nov 92 20:10:05 GMT
Article-I.D.: husc3.1992Nov6.151006.17167
Distribution: usa
Organization: Harvard University Science Center


     I would like to investigate the feasibility of using neural nets
to pick out certain features in photographs taken through a
microscope. The way it is done now, is to take a photo, and then go
over the desired features by hand (using a track-ball device). This is
a very difficult thing to formalize, but people can do it with lots of
practice. I want to look into the possibility of having the neural net
learn to do it. We have a database of thousands of sample cases built
up over the years (and already scanned into a computer). I'd like some
info on references to
1) books or papers that describe the kind of neural networks used for picking
out visual features (pattern recognition, classification, etc.)
2) descriptions of the algorithm used to feed an image to a network, etc.
3) any existing network software packages in the public domain, etc.
Please reply to mlevin@husc8.harvard.edu if you have any info.
Newsgroups: comp.ai.neural-nets
From: shj@swl.msd.ray.com (STEPHEN JOYCE)
Subject: NN Hardware Vendors
Summary: Identifying NN Hardware Vendors
Keywords: Neural Networks,Hardware,Vendors
Organization: Raytheon Company, Tewksbury, MA
Date: Mon, 9 Nov 1992 18:26:01 GMT

I'm looking for some information on 
identifying some Neural Network Hardware
Vendors, both digital and analog
implementations.
  Any company names would be appreciated.
From: esrgl@csv.warwick.ac.uk (Mr S J Sheppard)
Newsgroups: comp.ai.neural-nets
Subject: Re: Image Input for a Neural Net
Date: 13 Nov 1992 09:53:49 -0000
Organization: Computing Services, University of Warwick, UK
NNTP-Posting-Host: violet.csv.warwick.ac.uk

In article <1992Nov12.181625.4135@newssun.med.miami.edu> dbrown@newssun.med.miami.edu (Daniel Brown) writes:
>I'm looking for information about preprocessing for image
>inputs into a neural-net.
>
>Our project requires the use of a 480x480 24-bit color 
>image file in the tiff format.  Even after converting to
>a greyscale image, that's over 65,000 inputs if used
>directly.  I don't know too many systems that can handle
>that much information; certainly ours can't.
>
>I've looked a couple of compression techniques: i) summing
>and averaging over 100 pixels at a time (a coarse 
>version of the former image), ii) coarse coding similar
>to the latter), and iii) sampling of a FFT transform of
>the input data.
>
>The obvious goal is to use as much important information
>without overloading the system.  Does anyone have any other
>suggestions?
>

Try Principal Component Analysis!
I've been working on applying Neural Networks to the Diagnosis of 
Medical Images and have also been working with large input images. 
(Although perhaps not as large as these). Using PCA's I've managed 
to reduce the dimensionality of my data by upto 5x, but still being 
able to keep over 98% of the information.

Good Luck

Steve Sheppard

************************************************************************
**								      **
**	Steve Sheppard						      **
**	Research Postgraduate 	E-mail: esrgl@uk.ac.warwick.csv	      **
**	Dept of Engineering	Tele  : 44 - 203 - 523523 extn 2654   **
**	Warwick University	Fax   : 44 - 203 - 418922	      **
**	Coventry						      **
**	CV4 7AL							      **
**								      **
************************************************************************
From: wct1+@pitt.edu (William C Tom)
Newsgroups: comp.ai.neural-nets
Subject: NN software for sale
Date: 11 Nov 92 18:14:52 GMT
Distribution: usa
Organization: University of Pittsburgh

I would like to sell my copy of Brainmaker 2.3, by California
Scientific Software.

Asking $75, including dual disk sizes,  both manuals
  ( Cost me $140 new a year ago.)
------------------------------------------------------------------

wct1+@pitt.edu
From: moeller@iw.uni-hannover.de (Henning Moeller)
Subject: Q: Controlling a dynamic system
Reply-To: moeller@iw.uni-hannover.de
Organization: Institut fuer Werkstoffkunde, Uni Hannover
Date: Mon, 9 Nov 1992 16:05:49 GMT


Hi,
are there any references about a guide and control system
for a dynamic system (under water vehicle) based on feature
maps (Kohonen-modell) ?

Thanks in advance
                  Henning


"my second post to internet"


-----------------------------------------------------------------------	
|  Henning Moeller	         |  Tel: INT-49-511-762-3906   	      |
|  Universitaet Hannover         |  Fax: INT-49-511-762-5245	      |
|  Institut fuer Werkstoffkunde  |  Email: moeller@iw.uni-hannover.de |
|  Fachgruppe Unterwassertechnik |		   		      |
|  Appelstr. 11a  	         |                                    |
|W-3000 Hannover, GERMANY        |           			      |
-----------------------------------------------------------------------
From: isc30221@nusunix1.nus.sg (CHONG SOON FAI)
Subject: Kohonen (self-organizing) maps parameters
Organization: National University of Singapore
X-Newsreader: TIN [version 1.1 PL6]
Date: Fri, 6 Nov 1992 01:21:07 GMT

hi!  I m doing a project on Kohonen (self-organizing)
semantic/topological maps.  I am simulating the neural net in software
in software.

Does anyone have any suggestions as to what neural net parameters (eg
size of network, number of training vectors, size of neighbourhood, what
function to use for the learning coefficient etc) might be useful in
getting the net to converge?

Thanks!


SF
"my first post to internet" 
Newsgroups: comp.ai.neural-nets
From: pfsum@cucs18.cs.cuhk.hk (Sum Pui Fai)
Subject: Need Infomation
Keywords: Hopfield Network , Optimization
Organization: Faculty of Engineering, The Chinese U. of Hong Kong
Date: Fri, 6 Nov 1992 00:46:15 GMT

I would like to ask the netter for the infomation on the
APPLICATION OF HOPFIELD NETWORK IN SOLVING OPTIMIZATION
problem. If possible, not the TSP, not the graph partition
not the N-queen problem. I need some real application 
examples such as something like that application of 
Hopfield net in control and signal processing etc.

I will give a summary after I have gather all the reply.

Thanks in advance.

John Sum
--------------------------------------------------------------
John Sum

Department of Computer Science
Chinese University of Hong Kong
Shatin, Hong Kong.

e-mail : pfsum@cs.cuhk.hk           Tel : 852 609 7972
---------------------------------------------------------------
Newsgroups: comp.ai.neural-nets
Subject: Kohonen Algorithm used in ANSIM Neural Net Software
From: tuya@polar.etsiig.uniovi.es (Javier Tuya)
Date: 6 Nov 92 12:31:43 +0100
Organization: Universidad de Oviedo

I'm using the ANSim neural net software, with Delta FPP card for some 
simulations in speech recognition tasks.

I've used Kohonen network, an I have found that the algorithm used is not the
"standard" Kohonen algoritm. Also, I had some results showing that the "ANSim"
algorithm can be better (in some cases) than "standard" algorithm.



In "ANSIm" algorithm, the output for each cell j is

	out(j) = - SUM(x(i)*m(i,j) + Norm_Weights
                    i

The winner is the most negative cell.

The term Norm_Weights is:

	Norm_Weights(j) = 0.5 * SUM (m(i,j)*m(i,j))
                                 i


This is different to "standard" algorithm where:

	out(j) = SUM(x(i)*m(i,j)
                    i


Someon knows references (papers or books) where "ANSim" algoritm is used? 

I would appreciate also more details about the mathematical
foundations (ex. convergence proofs), because the classification
made with "ANSim" algorithm is different that "standard" algorithm.
For example, "ANSim" algorithm is more sensitive with the magnitude of input
vectors, due to the additive effect for Norm_Weights.


Thanks in advance

+--------------------------------------+------------------------------------+
| Javier Tuya                          | PSI:    PSI%(02145)285060338::TUYA |
| E.T.S. Ingenieros Industriales       | E-Mail: tuya@etsiig.uniovi.es      |
| Area de Lenguajes y Sistemas         | HEPNET: 16515::TUYA                |
| Informaticos (Universidad de Oviedo) |       : EASTVC::TUYA (area 16.131) |
| Carretera de Castiello s/n. 	       | Phone:  (..34-8) 518 2049          |
| E-33394 GIJON/SPAIN                  | FAX:    (..34-8) 533 8538          |
+--------------------------------------+------------------------------------+
From: jdd@duke.cs.duke.edu (John Dengler)
Newsgroups: comp.ai.neural-nets
Subject: conference posting
Date: 5 Nov 92 22:12:38 GMT
Organization: Duke University Computer Science Dept.; Durham, N.C.

Recently there was a posting which listed upcoming neural-network
related conferences.  Would someone please send me a copy of that
posting, or at least its message number?  Thanks in advance.


-- 
John Dengler, Computer Science Dept., Duke University, Durham, NC 27706 USA
ARPA:	jdd@cs.duke.edu
CSNET:	jdd@duke
UUCP:	decvax!duke!jdd
Newsgroups: comp.ai.neural-nets
From: andersen@elec.uq.oz.au (Hans Andersen)
Subject: FAQ list wanted, please.
Organization: Prentice Centre, University of Queensland
Date: Wed, 11 Nov 1992 00:05:29 GMT


I need a reference which is usually in the FAQ list, i.e. E-mail
address of the managing editor of "Complex Systems", Don DeLand.  
Could whoever posts the FAQ list please put it up again.

Thanks
Hans Andersen
Newsgroups: comp.ai.neural-nets