HDTenEightyP/NetNews huggingface dataset:
From: sakelley@euclid.ucsd.edu (Scott Kelley)
Newsgroups: comp.multimedia
Subject: Re: Help: JPEG images in MicroMind Dir.
Date: 12 Sep 92 17:45:37 GMT

In article <BuF73L.FMq@usenet.ucs.indiana.edu>, chenp@silver.ucs.indiana.edu (pai-lin chen) writes:
> Dear netters,
> 	I have a project which requires heavy use of still video in MicroMind
> Director.  To save the memory space that the color images may take, I took
> the folowing steps:
> 	First, I reduced the image size, resolution, so that each image will
> take about 450 K. Then I used QuickTime utility to compress the image, so that
> the size of each image is about 45K.  So far so good.  However, when I imported
> the image into MicroMind Dir. 3.1, the size of image turned back to 450K.
> 	It seemed that Director 3.1 decompressed the image; however, this is
> somewhat strange.  If MMD is QuickTime ready, it should be able to read the 
> file in JPEG format with original size.  Could someone out there give me a
> clue?  Thanks in advance.
> 
> Pai-lin Chen
> chenp@ucs.indiana.edu

What's happening here is this: You do an "Import" command on the tiny
jpeg file, Director reads it in as a PICT file (it gets automatically
decompressed by QuickTime) and Director stores the bitmap in it's own
format (the 450k size). One way around this is to use the "Link To File"
command, which will cause Director to read the picture in on demand. The
big drawback to using "Link to" is that it builds in hard pathnames and
so when you install your software on someone else's machine it tends not
to work right. In general, I've been fairly disappointed wiuth
Director's support of QuickTime, and the still compression in
particular, and for our project I'm writing a series of XObjects that
overcome Director 3.1's poor implementation of QuickTime.

Scott

-------------------------------------------------
Scott Kelley, sakelley@ucsd.edu, (619) 534-8560
Multimedia Interactive Learning Laboratory (MILL)
Department of Biology, UC San Diego
-------------------------------------------------
Newsgroups: comp.multimedia
From: ronniele@iti.gov.sg (Ronnie Lee Wah Sheun)
Subject: calling scripts from C/C++ ?
Organization: Information Technology Institute, National Computer Board, S'pore
Date: Wed, 9 Sep 1992 02:32:55 GMT

Can anyone tell me if there are any multimedia software packages
that allows me to design scripts (sound, video, animation, etc)
which can be called by a C or C++ program?  The platform is
preferably PC (Windows) but Macintosh will do too.  Thanks!

Ronnie Lee
Information Technology Institute, Singapore
Email: ronniele@iti.gov.sg

Xref: sparky comp.ai:3469 alt.hypertext:767 comp.ai.edu:420
From: wagener@bcstec.ca.boeing.com (Pamela Pincha-Wagener)
Newsgroups: comp.ai,alt.hypertext,comp.ai.edu
Subject: Re: Number of Expert Systems currently in use
Date: 15 Sep 92 21:26:48 GMT
Followup-To: comp.ai
Distribution: comp,alt
Organization: Boeing Computer Services, Seattle

Actually the government is a good place to go looking for
expert systems currently in use. Places to look are
the departments of Treasury (IRS especially), Interior( Bureau
of Mines, U.S.Geological Survey), and the FBI. These all
have systems in place. Some of the systems have been around
for at least 5 years.

The petroleum industry still has a few system up and running.
I've not kept up with what is currently in the works
but i have been hearing a lot more of neural net and
fuzzy logic systems.

Europe seems to have enbraced the technology and started
integrating it within their computing.

The Sabre system that American Airlines and other
airline use for reservations is an expert system
hybrid and is still very much in use.  Also I believe
that American Express's system for validating one
purchace is still in use and it's definitely an
expert system.

There are system out there. It's just everyone is keeping
mum about them.

Pam Pincha-Wagener
From: bakker@cs.uq.oz.au (Paultje Bakker)
Newsgroups: comp.ai.neural-nets
Subject: Re: Info requested on the next IWANN
Date: 14 Sep 92 23:03:32 GMT
Reply-To: bakker@cs.uq.oz.au
Organization: Depts. of Computer Science & Psychology, The University of Queensland, Brisbane, Australia

In article <1992Sep13.193127.12828@gdr.bath.ac.uk> mascdb@gdr.bath.ac.uk (C D Burdorf) writes:
>
>Does anyone have information on the next International Workshop
>on Artificial Neural Networks (IWANN) including paper submission info.
>

International Workshop on Artificial Neural Networks (IWANN'93)
	9-11 Jun 93     Sitges, Spain           30 Nov 92
Contact: cabestan@eel.upc.es (Prof. J.Cabestany)

paultje
--
(paul) bakker@cs.uq.oz.au     >  "Although the Australian accent does
computer science/ psychology  >   not lend itself as a pointer to
university of qld 4072        >   intellect, ... "
terra australis               >   The (British) Sunday Telegraph, 1992
From: 86u0364@csdvax.csd.unsw.edu.au
Newsgroups: comp.ai.neural-nets
Subject: Neural Network Companies
Date: 12 Sep 92 14:03:17 +1000
Organization: University of New South Wales

Hi
I was wondering if anyone could tell me the contact addresses, phone numbers
and fax numbers of the following Companies that deal with neural network
hardware and/or software.

Company					Neural Network of interest

Syntonics Systems Inc. 			DENDROS -1
Ward Systems Group 			NeuroShell
DAIR Computer Systems			NetWurkz
TRW 					TRW Mark V Neural Processor
Neurix Inc.				MacBrain/ HyperBrain


Many thanks
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 2033
Australia.
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 									

Newsgroups: comp.ai.neural-nets
From: palachar@bert.eecs.uic.edu (Prasad Palacharla)
Subject: Re: phone # IEEE Neural net council
Organization: University of Illinois at Chicago
Distribution: na
Date: Fri, 11 Sep 1992 18:53:01 GMT

saeed@a.cs.okstate.edu (SAEED FAISEL) writes:

>	I need the phone number of the IEEE Neural net council
>	or any body else connected to
>		IEEE Int'l Conference on Fuzzy Systems (1993)

>	Thanks in advance.
>	-saeed-
>-- 

>Faisel Saeed

>Computer Science Department,   Oklahoma State University,   Stillwater, OK 


The Phone # for IEEE Neural Networks Council is (619) 453-6222


Prasad Palacharla
Univ. of Illinois at Chicago.
Newsgroups: comp.ai.neural-nets
From: pedersen@seas.smu.edu (Ted Pedersen)
Subject: Neural Nets applied to Machine Translation?
Organization: SMU School Of Engineering and Applied Science
Date: Mon, 14 Sep 1992 21:15:34 GMT

I have been interested in Machine Translation for some time and have
implemented a few small systems in the last few years. I am interested
in finding out if anyone knows of Machine Translation systems that
employ neural networks?

Just for the record Machine Translation refers to the automatic (or
semi automatic) translation of one human language into another. Most
of my work has dealt with translating English into Spanish...Hope this
makes some sense. Anyone who has any experience or thoughts on the
above please drop me a note.

Thanks,
Ted Pedersen
From: bryant@neural.Kodak.COM (Steve Bryant)
Newsgroups: comp.ai.neural-nets
Subject: Adaptive data collection
Date: 15 Sep 92 14:15:50 GMT
Article-I.D.: kodak.1992Sep15.141550.5425
Organization: Eastman Kodak Co., Rochester, NY

Has anyone done work on adaptive data collection to reduce the number of redundant data samples. We have a situation were the data is fairly well grouped so that each sample taken is usually similar to data samples already taken. To collect enough samples to cover the sample space results in a gaussian distribution with most of the samples near the "average" of the data space with only a few data points covering the the far away points.

As a result, the network converges very quickly for new samples near to the average, but can take some time to learn the outliers. What I am planning on doing is to start with a small training set and train the network. This network will probably perform poorly. Collect only those new samples that are incorrect or do not pass some threshold criteria. Add these additional samples to my original set and retrain the network. Repeat this cycle until the network performs to the desired level. I perceive that th





is will create a data set that is evenly distributed in the data space with out clumping of the data. and as a result should train faster with fewer samples.
Newsgroups: comp.ai.neural-nets
From: ezli@ntuix.ntu.ac.sg (Li Ziqing)
Subject: Nonlinear Diff. Equ.
Organization: Nanyang Technological University - Singapore
X-Newsreader: Tin 1.1 PL4
Date: Wed, 9 Sep 1992 14:39:19 GMT


Newsgroups: comp.ai.neural-nets
From: R13552@paccvm.corp.mot.com (Taylor Cheng)
Subject: Re: references on boltzmann/hopfield net in control
Organization: Motorola
Date: 6 Sep 1992 17:10:32 HKT

Hello Sum,

     Is there any special reason for you to choose either Hopfield
and/or Boltzmann implementation for your project? Anyway, I've done
some research works using neural networks for robot arm control. Here
is a good reference (A number of Book reviews shared my opinion.)

Miller, W.T., Suttion, R.S. and Werbos, P.J.,"Neural Networks for
Control", MIT Press, Cambridge, MA, (1990).

Also look for the conference proceedings on IEEE International
Conference on Neural Networks.
From: hcbarth@afterlife.ncsc.mil (Bart Bartholomew)
Newsgroups: comp.ai.neural-nets
Subject: BEP question
Date: 7 Sep 92 06:42:41 GMT
Organization: The Great Beyond


	I have been pondering a problem in NNs that I think should
work, but I don't know how to implement it.  While I will describe
a specific problem, the technique (if there is one) should have
a fairly wide use.
	Everyone is just born knowing that sine waves are mean zero,
variance one.  If gaussian noise is added, the variance goes up.
Some people use the variance as a measure of the signal-to-noise ratio.
	Seems like we should be able to do an epochal forward pass
in which we accumulate the output points, compute the variance, 
develope an error term from the putative norm, and backpropagate that.
	One can, of course, see other such error terms.  In effect, 
we have some black box in the forward pass to develop the error for
the backward pass.
	The mathematicians (I am most assuredly *not*) will probably
object if the error term is not differentiable.  My wetware feeling
(notoriously unreliable) is that if a given function is not nicely
differential, there is some reasonable transformation of it that is.
	At this point we come to the inevitable clash with the nuts-
and-bolts of the problem in deciding how to actually implement
something like this.
	Your comments please.
	Bart
-- 
 If there's one thing I just can't stand, it's intolerance.
*No One* is responsible for my views, I'm a committee. Please do not
infer that which I do not imply.  hcbarth@afterlife.ncsc.mil
Newsgroups: comp.ai.neural-nets
From: saeed@a.cs.okstate.edu (SAEED FAISEL)
Subject: phone # IEEE Neural net council
Organization: Oklahoma State University, Computer Science, Stillwater
Distribution: na
Date: Thu, 10 Sep 92 18:00:22 GMT

	I need the phone number of the IEEE Neural net council
	or any body else connected to
		IEEE Int'l Conference on Fuzzy Systems (1993)

	Thanks in advance.
	-saeed-
-- 

Faisel Saeed

Computer Science Department,   Oklahoma State University,   Stillwater, OK
From: demers@cs.ucsd.edu (David DeMers)
Newsgroups: comp.ai.neural-nets
Subject: Re: function approximation with neural nets : what is right ?
Date: 10 Sep 92 18:53:09 GMT
Organization: =CSE Dept., U.C. San Diego

In article <1992Sep10.153652.12503@noose.ecn.purdue.edu> kavuri@lips2.ecn.purdue.edu (Surya N Kavuri ) writes:

  
>   In using a neural net to approximate a function, it is done with
>   out any a priori information on what class of functions we are 
>   looking for.  For example, if I know the function is some 
>   polynomial, there is no way to impose this condition on the net.

>   Is not this a disadvantage ?

It's the "disadvantage" accruing to all non-parametric methods.
It's more of using the wrong tool.  If you know that the
true function is a polynomial of a certain order, you will do 
better by just estimating the parameters.  But if you *think* it's 
a polynomial, and you are wrong (about the order, at least),
then even the best approximation you make will be poor.  

So use a parametric technique if you have a good idea of the
model class, and use a non-parametric technique if you have
no a priori information.


-- 
Dave DeMers			 ddemers@UCSD   demers@cs.ucsd.edu
Computer Science & Engineering	C-014		demers%cs@ucsd.bitnet
UC San Diego					...!ucsd!cs!demers
La Jolla, CA 92093-0114	(619) 534-0688, or -8187, FAX: (619) 534-7029
Newsgroups: comp.ai.neural-nets
From: thibault@kira.emba.uvm.edu (Scott A Thibault)
Subject: Hyperplanes
Organization: University of Vermont, EMBA Computer Facility
Date: Tue, 8 Sep 1992 15:56:11 GMT

  I've read a lot of things that refer to hyperplanes.  How do you display
I hyperplane given an energy function?

--Scott Thibault
University of Vermont
thibault@emba.uvm.edu
From: dd@doc.ic.ac.uk (Demetrius Dracopoulos)
Newsgroups: comp.ai.neural-nets
Subject: looking for a reference
Keywords: back propagation, training, generalisation
Date: 7 Sep 92 10:52:46 GMT
Reply-To: dd@doc.ic.ac.uk (Demetrius Dracopoulos)
Organization: Department Of Computing, Imperial College, London UK
NNTP-Posting-Host: swan.doc.ic.ac.uk


I'm looking for the complete reference of :

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

Thanks in advance.

Dimitri

-------------------------------------------------------
Don't mess with Texas
-------------------------------------------------------

*****     **    **   **    **   ******    *****      **
**  **    **    *** ***    **     **      **  **     **
**  **    **    ** * **    **     **      *****      **
*****     **    **   **    **     **      **  **     **
Newsgroups: comp.ai.neural-nets
From: mascdb@gdr.bath.ac.uk (C D Burdorf)
Newsgroups: comp.ai.neural-nets
Subject: Info requested on the next IWANN
Date: 13 Sep 92 19:31:27 GMT
Organization: University of Bath, UK


Does anyone have information on the next International Workshop
on Artificial Neural Networks (IWANN) including paper submission info.

Thanks,

Chris
Newsgroups: comp.ai.neural-nets
From: william@kub.nl (William Verkooijen)
Subject: New book on artificial Neural nets
Date: Tue, 15 Sep 92 07:55:05 GMT
Organization: KUB, The Netherlands

Hello,

Has anyone read the new book of H.White Artificial Neural Networks
published in august 1992?

If you have, I like to hear your reaction about it. I am thinking
about buying it. 

Any reaction is appreciated!
Thanks,
 
William Verkooijen
E-mail: william@kub.nl



Newsgroups: comp.ai.neural-nets
From: styri@hal.nta.no (Haakon Styri)
Subject: Re: source code in "neural network pc-tools" book
Reply-To: styri@nta.no
Organization: Norwegian Telecom Research
Date: Tue, 15 Sep 92 07:53:29 GMT

I wrote:
>
> Yup, I've seen at least one copy of this code on the net (ftp archives).
> However, the code is copyright protected if I'm not too wrong.

Ooops! I was very wrong indeed. To quote the copyright notice:
	"*** SHAREWARE ***
	 You may distribute unmodified copies of the diskette or software.
	 If you find the software of value, please send $20 or any amount,
	 to :-
		[Roy W. Dobbins old address deleted]"

I'll make penance by putting the nnpct companion code on my ftp-archive
asap. I hope I didn't create any inconvenience by my mistake.

(No, you don't know my ftp archive address ... yet.)

---
Haakon Styri
From: siva@a.cs.okstate.edu (KAVUTURU SIVA RAMA)
Newsgroups: comp.ai.neural-nets
Subject: search for an article from INNC,1990
Date: 10 Sep 92 14:47:27 GMT
Followup-To: siva@a.cs.okstate.edu
Organization: Oklahoma State University, Computer Science, Stillwater


     Hai,
           I am searching an article "Formal Neural Networks as an Adaptive
           Model for Water Demand" from INNC, 1990. I don't have this article's
           copy. Can any one, please, help me in acquiring this article.
           This INNC is at Paris in 1990.

           Thank you.
Newsgroups: comp.ai.neural-nets
From: swarnama@iti.gov.sg (S. Swarnamala)
Subject: RBF Nets - some basic issues 
Reply-To: swarnama@iti.gov.sg
Organization: Information Technology Institute, National Computer Board, S'pore
Date: Mon, 7 Sep 1992 01:53:09 GMT


Hello fellow netters,

I am trying to use RBF-based hybrid network for prediction. I follow the
design as is given in literature - Single hidden layer feed forward BP-net 
with linear output nodes and guassian hidden nodes. I also use additional nodes each of input dimension for every hidden unit (introduced for improving prediction). I have simulated this net using around 100 guassians for a 12 dimensional input of around 400 input sample size (could be oversized - I don't know). 

I have some fundamental questions regarding this :

 1) The unsupervised part of learning in this context is basically a process of clustering all the input patterns of N-dimension. The common way of quantifying the tightness of clustering is sum((Eu)dist of each pattern from its closest cluster center). What should this measure be for a good clustering?  OR in other words "Is there a bench mark against which the quality for a given clustered sample could be compared ( such as Average Relative Variance for prediction)"

 2) Is there any empirical relation between no: of attractors (cluster centres), input pattern dimension and no: of patterns to be clustered ?

 3) Any figures of reasonable input dimension for which RBF network size will be manageble ?

I request for answers to these queries . Also am thankful for any pointers to literature discussing these issues. I shall summarise the replies I get on this news-group.

PS: To my previous posting of this note, to my dissapointment I received no replies. Has the interest on RBF nets died out? If so, could any one summarise
the reasons for it. 
    
 Thanks and Regards,
  Mala.

-----------------------------------------
 Srirengan Swarnamala,                       
 Knowledge Systems Lab,
 Information Technology Institute,
 National Computer Board of Singapore.
 E-mail : (Internet) swarnama@iti.gov.sg



Newsgroups: comp.ai.neural-nets
From: block@dxlaa.cern.ch (Frank Block)
Subject: Re: Summary of CUPS + new question
Reply-To: block@dxlaa.cern.ch (Frank Block)
Organization: CERN, European Laboratory for Particle Physics, Geneva
Date: Fri, 11 Sep 1992 13:00:22 GMT


In article <arms.716190162@spedden>, arms@cs.UAlberta.CA (Bill Armstrong) writes:
[...text deleted...]
|> One wants a global minimum.  But doing the computations of
|> gradient descent more accurately, based on an entire epoch, guarantees
|> that you come to rest at the local minimum of the valley you started
|> in.  So why not do a faster computation that has a chance of kicking
|> the system out of the valley you are currently in?

It is not that we just have two options:

     - update the weights pattern per pattern
     - update the weights after looping over the whole trainset

We can also update the network after some patterns. For instance you have
a trainset containing 1000 patterns and you update the weights after ten
patterns. This will prevent the net from running into local minima.
But also if the weights are updated after presenting the whole data set
this does not have to imply the net gets stuck into local minima. If you,
and this is really important, present the patterns in random order this
is already a good medicine against local minima. 

|> I should add that there are other heuristics in the ALN algorithm that
|> are not gradient-descent type (atree release 2.7 on-line help,
|> technical notes on the learning algorithm).  I.e. some nodes are made
|> responsible and adaptations are caused to occur even in cases where
|> that could increase the error.  This is quite different from the
|> approach of adding noise to kick the system out of local minima,
|> because the kick is given in a promising direction according to the
|> heuristics.

Perhaps you could explain in a few words which is the idea behind this method.
How do you kick a network out of a local minimum into a 'promising direction'
(by which you probably mean the direction where the global minimum is located)?
How do you know which direction is promising?

Thanks
	Frank Block

===============================================================================
Frank Block
Div. PPE				BLOCKF@vxcern.cern.ch
CERN				e-mail: 
CH-1211 Geneve 23			BLOCKF@cernvm.cern.ch
Switzerland
===============================================================================
Newsgroups: comp.ai.neural-nets
From: rao@acs.ucalgary.ca (Harish Rao)
Subject: Neural Nets for Data Prediction???
Date: Fri, 11 Sep 92 22:17:30 GMT
Distribution: Comp.ai.neural-nets
Organization: The University of Calgary, Alberta


	Dear NN punters,

			I have a question for u nn gurus out there.
	We want to try and use NN for data prediction.
	The problem is something like this:

	If you have three variables say temp., pressure and stress
	and have data to depict the relationship between temp. and pressure
	for specific values of the stress..then is it possible to 
	predict the relationship between temp. and pressure
	for another value of stress? 
	Can this be done for more than 3 variables???

	Thanx

	Harish
From: sunjain@matt.ksu.ksu.edu (mera bharat mahan)
Newsgroups: comp.ai.neural-nets
Subject: source code in "neural network pc-tools" book
Date: 11 Sep 1992 15:45:05 -0500
Organization: Kansas State University
NNTP-Posting-Host: matt.ksu.ksu.edu

hi. 

  Has anybody taken the pains to write the source code for back-propagation
and kohenon algorithm given in the appendix A and B of "Neural Network PC Tools"
book?

I am not able to get hold of Dr. Dobbins to request for the diskette. 
( I am just a beginner and need to do some assignments for back-propagation this
  weekend ! )

I'll appreciate if somebody who has written the code, can provide me.

thanks a lot.

sunil jain.
Newsgroups: comp.ai.neural-nets
From: freek@cv.ruu.nl (Freek Beekman)
Subject: email adress Kostas Diamantras
Date: Tue, 15 Sep 1992 08:23:36 GMT
Organization: University of Utrecht, 3D Computer Vision Research Group

I should like to know how to get the Ph.d. Thesis of Kostas Diamantras
about Neural nets and statistical methods. Does abyone know his 
email adress hor knows how to get his thesis?

Thanks,

Freek

--
	 -   Freek J. Beekman -

	 Department of Radiology and Nuclear Medicine/3D ComputerVision
	 Utrecht University Hospital, Room E02.222
	 Heidelberglaan 100
	 3584CX Utrecht
	 The Netherlands
	 Phone +31-30-506711/506053
	 Fax   +31-30-513399
	 email freek@cv.ruu.nl
	 ~

Newsgroups: comp.ai.neural-nets