HDTenEightyP/NetNews huggingface dataset:
From: bat@dstos3.dsto.gov.au
Newsgroups: comp.ai.neural-nets
Subject: Mature Neural Applications ??
Date: 19 Nov 92 11:17:27 CST
Organization: Defence Science and Technology Organisation

I am trying to gain some perspective on the maturity of the Neural Network
market.  Does anyone have any knowledge about any mature neural applications.

By mature I mean a shrink wrapped application that incorporates a neural engine,
for example perhaps someones character recognition software, signal recognition
package  or similiar.  

					Brenton Thomas
From: rs69@cunixb.cc.columbia.edu (Rong Shen)
Newsgroups: comp.ai.neural-nets
Subject: How to train a lifeless network (of "silicon atoms")?
Date: 21 Nov 92 00:26:54 GMT
Organization: Columbia University

Your Highness:

	Please allow me to ask you this childish question:

	Suppose you have a neural network and you want to train it to
perform a task; for the moment, let's say the task is to recognize
handwriting. Now suppose the network has recognized the word "hello,"
and the weight in the synapse between neurodes (neurons) X and Y is k.
If you proceed to train the network to recognize the word "goodbye"
(by back propagation, or whatever algorithms), and since all the
neurodes are connected in some way (through some interneurons, maybe),
the synaptic weight between X and Y is likely to change from k to some
other number; similarly, the weights in other synapses will change.
Therefore, it is extremely likely that one training session will erase
the efforts of previous sessions.

	My question is, What engineering tricks shall we use to
overcome this apparent difficulty?

	Thanks.

--
rs69@cunixb.cc.columbia.edu
Newsgroups: comp.ai.neural-nets
From: erik@maui.til.com (Erik Horstkotte)
Subject: Re: Creating GA group.
Keywords: GA
Organization: Togai InfraLogic, Inc.
Date: Wed, 18 Nov 1992 16:39:54 GMT

Didn't Mike Arras already post an RFD for comp.ai.genetic?
-- 
Erik Horstkotte, Togai InfraLogic, Inc.
The World's Source for Fuzzy Logic Solutions (The company, not me!)
erik@til.com, gordius!til!erik - (714) 975-8522
info@til.com for info, fuzzy-server@til.com for fuzzy mail-server
Newsgroups: comp.ai.neural-nets
From: kattan@Jane.UH.EDU (Mike Kattan)
Subject: Neural Net Graphics Package Still Isn't Ready
Reply-To: kattan@Jane.UH.EDU
Organization: University of Houston
Date: Mon, 9 Nov 1992 18:06:55 GMT

The author is currently re-writing the package to make it run faster.  This may
take another week or two.  As soon as the demo version is ready, I'll put it up
at an ftp site and post instructions here.

-------------------------------------------------------------------------
Mike Kattan // Decision and Information Sciences // University of Houston
Houston, TX  77204-6282 // (713) 743-4734 // Fax: -4693 // kattan@uhupvm1
=========================================================================
From: karun@faline.bellcore.com (N. Karunanithi)
Newsgroups: comp.ai.neural-nets
Subject: Need reference..
Date: 23 Nov 92 19:31:25 GMT
Reply-To: karun@faline.bellcore.com (N. Karunanithi)
Organization: Bellcore MRE


Dear folks,
      I came across the title of an article which describes
the use of neural networks  for VLSI fault modeling/testing.

Title : A Neural Network Model for Fault-Diagnosis 
        of Digital Circuits"

Authors: Jakubowicz, Oleg and Ramanujam, Sridhar

Would like to know the proceedings of the conferecnec in which
it appeared. If by chance any of the authors come across this
request please send me a copy of it to my address.

      N. Karunanithi
      MRE 2E-378
      Bellcore
      445, South Street
      Morristown, NJ 07692

THanks in advance,
-Karun
Newsgroups: comp.ai.neural-nets
From: mikek@fred.Colorado.EDU (Mike Kranzdorf)
Subject: I need a NIPS registration form, please.
Originator: mikek@fred
Organization: University of Colorado Boulder, OCS
Distribution: usa
Date: Tue, 17 Nov 1992 17:31:15 GMT

I have no trouble finding a schedule on the net, but I can't find
a registration form anywhere. Would someone please post one or email
me one? Thanks much,
mike kranzdorf

internet: mikek@boulder.colorado.edu
uucp:{ncar|nbires}!boulder!mikek
Newsgroups: comp.ai.neural-nets
From: sgrenander@NASAMAIL.JPL.NASA.GOV (Sven Grenander)
Subject: Re: How to train a lifeless network (of "silicon atoms")?
Followup-To: comp.ai.neural-nets
Organization: Jet Propulsion Laboratory
Date: Mon, 23 Nov 1992 21:01:45 GMT

In article <1992Nov22.215822.7238@news.columbia.edu>,
rs69@cunixb.cc.columbia.edu (Rong Shen) wrote:
> 
> In article <1992Nov22.182325.24185@dxcern.cern.ch> block@dxlaa.cern.ch (Frank Block) writes:
> 
> 			(junk deleted)
> 
> >What you normally do during training is to present (taking you example) the
> >words 'hello' and 'goodbye' alternatively. You should not train the net first
> >just on one and then, when it has learned to recognize it, on the other.
> >The training is a statistical process which in the end (let's hope) converges
> >to a good set of weights (a compromise which recognizes all patterns in an
> >optimal way).
> 
> 	Thanks, Frank.
> 
> 	If I feed the words alternately, how would I train the network
> to recognize 99,999 words? Would not the 99,999th word erase the 1st
> word?
> 
> --
> rs69@cunixb.cc.columbia.edu

It would if your learning rate is too high. Using BrainMaker I have found
that I have to reduce the learning (training ?) rate from the default 1.0
to as little as .01 when presenting a large training set (~50,000 separate
facts). I suspect that if all these training facts were as different as
'hello' and 'goodbye' the learning rate may have to be reduced ever more.

-Sven
From: sam@zoar.sarnoff.com (Scott A. Markel  x2683)
Newsgroups: comp.ai.neural-nets
Subject: NIPS 92 Workshop on Training Issues
Date: 19 Nov 92 16:53:04 GMT
Followup-To: comp.ai.neural-nets
Organization: /home/zoar/sam/.organization

****************************   NIPS 92  Workshop   *****************************

                 "Computational Issues in Neural Network Training"
                 
                                       or

                   Why is Back-Propagation Still So Popular?

********************************************************************************

Roger Crane and I are are leading a NIPS '92 workshop on "Computational Issues
in Neural Network Training".  Our workshop will be on Saturday, 5 December, the
second of two days of workshops in Vail.

The discussion will focus on optimization techniques currently used by neural
net researchers, and include some other techniques that are available.  Back-
propagation is still the optimization technique of choice even though there are
obvious problems in training with BP: speed, convergence, ... .  Several innova-
tive algorithms have been proposed by the neural net community to improve upon
BP, e.g., Scott Fahlman's QuickProp.  We feel that there are classical optimiza-
tion techniques that are superior to back-propagation.  In fact, gradient
descent (BP) fell out of favor with the mathematical optimization folks way back
in the 60's!  So why is BP still so popular?

Topics along these lines include:

   *  Why are classical methods generally ignored?

   *  Computational speed

   *  Convergence criteria (or lack thereof!)

Broader issues to be discussed include:

   *  Local minima

   *  Selection of starting points

   *  Conditioning (for higher order methods)

   *  Characterization of the error surface

If you would like to present something on any of these or similar topics, please
contact me by e-mail and we can discuss details.

Workshops are scheduled for a total of four hours.  We're allowing for approxi-
mately 8 presentations of 10-20 minutes each, since we want to make sure that
ample time is reserved for discussion and informal presentations.  We will
encourage (incite) lively audience participation.  By the way, none of the NIPS
workshops are limited to presenters only.  People who want to show up and just
listen are more than welcome.

--
Scott Markel                   Computational Science Research
smarkel@sarnoff.com            David Sarnoff Research Center
Tel. 609-734-2683              CN 5300
FAX  609-734-2662              Princeton, NJ 08543-5300
From: kostas@siemens.com. (Konstantinos Diamantaras)
Newsgroups: comp.ai.neural-nets
Subject: Re: principal component analysis and "wavelets"
Date: 18 Nov 92 16:11:33 GMT
Distribution: usa
Organization: Siemens Corp. Res., Inc.

In article <1992Nov17.185852.5738@newssun.med.miami.edu> dbrown@newssun.med.miami.edu (Daniel Brown) writes:
>I posted about a week ago with a question about preprocessing
>of image input.  I only received a few responses, and I need
>more information on them:
>
>	i) What is principal component analysis (PCA)?

I am not much of a wavelet person, but I can tell you what PCA is.
It is related to the Karhunen-Loeve transform, and eigenvalue decomposition
of the autocorrelation matrix of your input. Simply put, if you
have an n-dim stochastic signal x with autocorrelation matrix R=E{xx'}
and you look to map it LINEARLY down to m dimensions (where m<n)
via a transformation of the form
y=Wx
(W is an mxn matrix) so that when you optimally reconstruct x from y
you get the minimum mean-square error possible, then the best matrix
to use is W=[e_1 e_2 e_3 ... e_m]' where e_i are the principal eigenvectors
of R (i.e. those eigenvectors associated with the maximum eigenvalues).
The minimal error you get is just the sum of the smallest eigenvalues of R.
That is a classic result originally shown by Hotelling (1933) and
was taken over by Karhunen and Loeve (in the 50's I think) for the analysis
of continuous-time stochastic processes.

You can find discussions regarding the Karhunen-Loeve transform
(which is really the same as PCA) in any good book on image processing
theory, such as Anil K. Jain, "Fundamentals of Digital Image Processing",
Prentice Hall, 1989, or Arun N. Netravali and Barry G. Haskel,
"Digital Pictures: Representation and Compression", Plenum Press, 1988,
among many others.

>	ii) Has anyone heard of "wavelets" used in 
>	   preprocessing of image data?
>
>Thanks in advance,
>
>Dan Brown
>dbrown@newssun.med.miami.edu
>
>I will post a summary of replies ...

Hope this helps.

Best regards

		Kostas Diamantaras
From: block@dxlaa.cern.ch (Frank Block)
Newsgroups: comp.ai.neural-nets
Subject: Re: How to train a lifeless network (of "silicon atoms")?
Date: 22 Nov 92 18:23:25 GMT
Reply-To: block@dxlaa.cern.ch (Frank Block)
Organization: CERN, European Laboratory for Particle Physics, Geneva


In article <1992Nov21.002654.13198@news.columbia.edu>, rs69@cunixb.cc.columbia.edu (Rong Shen) writes:

|> 	Please allow me to ask you this childish question:
|> 
|> 	Suppose you have a neural network and you want to train it to
|> perform a task; for the moment, let's say the task is to recognize
|> handwriting. Now suppose the network has recognized the word "hello,"
|> and the weight in the synapse between neurodes (neurons) X and Y is k.
|> If you proceed to train the network to recognize the word "goodbye"
|> (by back propagation, or whatever algorithms), and since all the
|> neurodes are connected in some way (through some interneurons, maybe),
|> the synaptic weight between X and Y is likely to change from k to some
|> other number; similarly, the weights in other synapses will change.
|> Therefore, it is extremely likely that one training session will erase
|> the efforts of previous sessions.
|> 
|> 	My question is, What engineering tricks shall we use to
|> overcome this apparent difficulty?
|> 
|> 	Thanks.
|> 
|> --
|> rs69@cunixb.cc.columbia.edu
|> 
--

What you normally do during training is to present (taking you example) the
words 'hello' and 'goodbye' alternatively. You should not train the net first
just on one and then, when it has learned to recognize it, on the other.
The training is a statistical process which in the end (let's hope) converges
to a good set of weights (a compromise which recognizes all patterns in an
optimal way).
The engineering trick is mostly the so-called 'gradient descent' (in backprop). 
This moves you current weight vector always in a direction which decreases the 
network error measure.

Hope this helps a bit

Frank

===============================================================================
Frank Block
Div. PPE
European Laboratory for Particle Physics - CERN
CH-1211 Geneve 23					BLOCKF@cernvm.cern.ch
Switzerland						BLOCKF@vxcern.cern.ch
===============================================================================
Newsgroups: comp.ai.neural-nets
From: holly@wowbagger.pc-labor.uni-bremen.de (Holger Duerer)
Subject: Re: Neural Nets and Visual tracking?
Organization: PC-Labor der Universitaet Bremen
Date: Mon, 16 Nov 1992 15:34:13 GMT

Well, 
we are a bit behind with the news but here I go anyway:

(someone asked:)
>        I'm looking for references on the application of Neural-networks
>to visual-tracking. In particular, the ability to predict or anticipate
>futur positions based on information about the current history of the 
>trajectory as well as THE DEVELOPMENT of this ability.

I'm currently reading a paper from Jonathan A. Marshall (Neural Networks,
Vol 3, pp45-74, 1990) titled:
  "Self-Organizing Neural Networks for Perception of Visual Motion"

Would that be what you are looking for?

	Holger
--
----------------------------------------------------------------------
Holger D"urer                           <holly@pc-labor.uni-bremen.de>
 "Infinity is a notion best contemplated within four 
  walls with sheets tucked snugly around one's feet" -- Oliver Wendell Jones
Newsgroups: comp.ai.neural-nets
From: andersen@elec.uq.oz.au (Hans Andersen)
Subject: Re: Creating GA group.
Keywords: GA
Organization: Prentice Centre, University of Queensland
Date: Wed, 18 Nov 1992 01:16:13 GMT


Oops!  Someone just notified me that a GA group is about to be officially
proposed and is calling for votes next week.  In that case disregard the 
previous message that I wrote.

-Hans
Newsgroups: comp.ai.neural-nets
From: kaykit@iti.gov.sg (Chan Kay Kit (KSL))
Subject: Seeking email/postal address of ....
Reply-To: kaykit@iti.gov.sg
Organization: Information Technology Institute, National Computer Board, S'pore
Date: Tue, 17 Nov 1992 02:39:49 GMT

Hello everyone,

	I need help in finding either the

	(i) email address or
	(ii) postal address

of any one of the following persons:

	(i) Steven A. Harp
	(ii) Tariq Samad
	(iii) Aloke Guhua

They coauthored a paper entitled "Designing Application-Specific Neural Networks
Using the Genetic Algorithm" ,which appeared in NIPS II. The most recent postal
address I've got is

	Honeywell SSDC
	1000 Boone Avenue North
	Golden Valley, MN 55427

which I've been told is no longer valid.

Would appreciate any info I can get. Please email all replies to kaykit@iti.gov.sg.

Thanks!
 


---
Kay-Kit CHAN    			| Internet: kaykit@iti.gov.sg
Knowledge Systems Lab			| Bitnet:   kaykit@itivax
Information Technology Institute	| Tel:      (65) 772-0920
National Computer Board of Singapore    | Fax:      (65) 770-3043

Newsgroups: comp.ai.neural-nets
From: rkumar@spartan.ac.BrockU.CA (Rahul Kumar)
Subject: Reactive Keyboarding
Organization: Brock University, St. Catharines Ontario
X-Newsreader: Tin 1.1 PL4
Date: Tue, 17 Nov 1992 21:27:25 GMT


Would anyone happen to have on any thing on Reactive Keyboarding and 
Neural Nets.  
Reactive Keyboarding is the concept wherein, the software program 
predicts the next few characters that the user will type.  I know
there are many articles written for it by Dr. Darragh, from Calgary
University, but they are not related to neural networks. 
If someone can give me some hints as to where to look for them, I would
appreciate it greatly.

   Thanks in anticipation.


-- 



				  Rahul Kumar
From: spxmpap@thor.cf.ac.uk (Mr M P A Page 92)
Newsgroups: comp.ai.neural-nets
Subject: Carpenter and Grossberg's gated pacemaker.
Date: 17 Nov 92 17:01:29 GMT
Organization: University of Wales College of Cardiff, Cardiff, Wales, UK
X-Mailer: Cardiff Computing Maths PP Mail Open News Gateway

I am currently seeking to adapt the "gated pacemaker" model of
Carpenter and Grossberg (1983) to a simulation of musical rhythm
perception (a bit speculative, I admit).  I have written a program 
which models the dynamic behaviour of the neural circuit and 
have been messing around with the parameters.  Unfortunately, my 
program does not reproduce the linear relationship between the 
oscillator period and the transmitter release rate parameter, C5, as
described in the above paper.

Has anyone else simulated and used this model and, if so, did the 
parameters have the effect described?  In addition, has anyone tried
to synchronize the circuit's oscillations with some external rhythmic stimulus
of a given fixed period (within limits)? This is mentioned in one of
Grossberg's 1986 papers on serial order, masking fields, word
recognition etc. (I'm sorry I don't have the paper to hand).

Ref: A Neural Theory of Circadian Rhythms: The Gated Pacemaker
  Carpenter and Grossberg,  Biological Cybernetics. 48,pp.35-59(1983)


Thanks,

Mike Page, spxmpap@uk.ac.cardiff.thor  or m.page01@uk.ac.ic.ee
Newsgroups: comp.ai.neural-nets
From: muttiah@thistle.ecn.purdue.edu (Ranjan S Muttiah)
Subject: asymmetric T_ij (Hopfield network)
Organization: Purdue University Engineering Computer Network
Date: Wed, 18 Nov 1992 16:37:17 GMT


I'm looking for references that solve any energy function (preferrably
something not connected to the TSP :-) for an asymmetric weight matrix
in the Hopfield neural network.

Pl. send me email and I'll summarize.
Xref: sparky comp.ai.neural-nets:4309 ont.events:562
From: admiral@m-5.Eng.Sun.COM (Michael Limprecht)
Newsgroups: comp.ai.neural-nets
Subject: Re: Neural Nets and C++
Date: 20 Nov 92 21:31:57 GMT
Reply-To: admiral@m-5.Eng.Sun.COM
Organization: Sun Microsystems
NNTP-Posting-Host: m-5

In article 1oI@news.cso.uiuc.edu, gsbg9079@uxa.cso.uiuc.edu (G. Scott Braley) writes:
>clin@eng.umd.edu (Charles Lin) writes:
>
>
>
>>In article <1992Nov14.020053.10113@sbcs.sunysb.edu>, worrick@csws2.ic.sunysb.edu (William Orrick) writes:
>>>
>>>As a beginner who did buy the book I would add that it was not very
>>>useful to me either.  The code in the back of the book bug-ridden and
>>>the text provides little detailed information to help with the theory
>>>or the running of the programs.
>>>
>
>>  One problem that may cause this (other than carelessness) is that there
>>is no (as far as I know) standardized C++.    In any case, if this
>>is true, it is better to describe the principles, rather than have
>>the person just copy code.   
>
>>--
>>Charles Lin
>>clin@eng.umd.edu
>
>Actually, there were several subroutines left out of the book.  Contact your bookseller and
>ask they to contact Wiley, they will send a disk with the corrected code.
>
>			-Scott

I've just started playing around with this code. Can those of you who have
already tried using it post your bug fixes here? I will do the same.
Or send them to me and I will repost the collection of fixes.

Maybe between all of us we can get some use out of what looks 
like a bad situation.


Mick
-------------------------------------------------------------------
    Q: What's the difference between Sun and the Boyscouts?
    A: The Boyscouts have Adult supervision.
						-Scott McNealy 
-------------------------------------------------------------------
uucp: {anywhere}!sun!admiral or admiral@m-5.Eng.Sun.COM
-------------------------------------------------------------------
Newsgroups: comp.ai.nlang-know-rep
From: davisr@sundrops.cs.ucdavis.edu (Robert Davis)
Newsgroups: comp.ai,sci.math,sci.math.stat
Subject: Looking for Good Intro to Bayesian Classifiers
Date: 16 Nov 92 00:01:38 GMT
Followup-To: comp.ai
Distribution: usa
Organization: Department of Computer Science, University of California, Davis

I am looking for a book or paper with a good introduction into using 
Bayesian Classifiers preferably with a few examples showing how to 
determine the probabilities.  I understand the idea behind a Bayesian 
Classifier, but I have looked at a couple of examples of Bayesian 
Classifiers and they appeared to use maximum likelihood estimators to 
determine the values for the various probabilities.  According to my 
statistics book, this method provides excellent estimates if the sample 
size is large, but in the cases I hope to be dealing with I will only 
have a few samples and want to get as much information from them as I can.

There has also been some discussion here at UC Davis about whether 
there exist some values for the probabilities of the classes and the 
probabilities of the various features given the class, which tells 
you as much as possible from the given samples.  The methods I have 
seen require a previous distribution and show you how to update this 
distribution after seeing each example.  One then chooses a value from 
this distribution using their favorite function, such as mean, median, 
or mode, or by using a loss function.  From this it seems that their 
is no value for the probabilities which will give you as much
information as possible about the samples without chosing some
type of loss function.  Is this correct and is there some way to 
prove if it is or is not correct?

Please reply via email.  I'll appreciate any help I can get.

Robert Davis
Department of Computer Science
University of California, Davis
email:  davisr@cs.ucdavis.edu



Newsgroups: comp.ai
From: wfsq_ltd@uhura.cc.rochester.edu (Billy the Kidd)
Newsgroups: comp.ai,comp.ai.edu,sci.cognitive
Subject: B.S. Degree
Date: 18 Nov 92 03:36:42 GMT
Organization: University of Rochester - Rochester, New York


	I am currently in the process of pettitioning to get a B.S. degree in
Cognitive Science, as it is currently only offered as a B.A..  Much of my
curriculum consists of Computer Science, Elec. Engineering, and Math/Physics
courses - and for this reason I am curious as to why it is labeled an arts
degree, and not a science degree.  

	In short, I would like to hear from Cognitive Science Majors at other
Universities as to what your school offers, a BA or BS program.  You can 
post on any of the above mentioned groups, or email me at:

		wfsq_ltd@uhura.cc.rochester.edu

	Thanx, 
	
       Billy Kidd
Newsgroups: comp.ai,comp.ai.edu,sci.cognitive