HDTenEightyP/NetNews huggingface dataset:
From: visakan@eng.cam.ac.uk (Visakan Kadirkamanathan)
Newsgroups: comp.ai.neural-nets
Subject: Technical Report available
Keywords: Sequential learning, growing network
Date: 30 Sep 92 16:20:00 GMT
Organization: Cambridge University Engineering Department, UK

The following technical report is now available via ftp. It is placed in the 
Cambridge University ftp archive svr-ftp.eng.cam.ac.uk. The ftp instructions 
for retrieval follow the abstract. 

------------------------------------------------------------------------------
	A function estimation approach to sequential learning with
			neural networks


		Visakan Kadirkamanathan and Mahesan Niranjan


      		    Technical Report CUED/F-INFENG/TR.111

		 Cambridge University Engineering Department
			Cambridge CB2 1PZ, England



				ABSTRACT

In this paper, we investigate the problem of optimal sequential learning, 
viewed as a problem of estimating an underlying function sequentially rather 
than estimating a set of parameters of the neural network. 

Firstly, we arrive at a sub-optimal solution to the sequential estimate which 
can be mapped by a growing Gaussian radial basis function (GaRBF) network. 
This network adds hidden units for each observation. The function space 
approach in which the estimates are represented as vectors in a function 
space, is used in developing a growth criteria to limit its growth. 
A simplification of the criterion leads to two joint criteria on the distance 
of the present pattern and the existing unit centres in the input space and 
on the approximation error of the network for the given observation to be 
satisfied together. This network is similar to the resource allocating network 
(RAN) and hence RAN can be interpreted from a function space approach to 
sequential learning. 

Secondly, we present an enhancement to the RAN. The RAN either allocates a 
new unit based on the novelty of an observation or adapt the network 
parameters by the LMS algorithm. The function space interpretation of the RAN 
lends itself to an enhancement of the RAN in which the extended Kalman filter 
(EKF) algorithm is used in place of the LMS algorithm. 
The performance of the RAN and the enhanced network are compared in the 
experimental tasks of function approximation and time-series prediction 
demonstrating the superior performance of the enhanced network with fewer 
number of hidden units. The approach adopted here has led us towards the 
minimal network required for a sequential learning problem. 

------------------------------------------------------------------------------

FTP INSTRUCTIONS:

unix> ftp svr-ftp.eng.cam.ac.uk
Name: anonymous
Password: (send userid@site)
ftp> cd reports
ftp> binary
ftp> get kadirkamanathan_tr111.ps.Z
ftp> quit
unix> uncompress kadirkamanathan_tr111.ps.Z
unix> 

--
Dr.Visakan Kadirkamanathan                    email: visakan@uk.ac.cam.eng
Cambridge University Engineering Department
Trumpington Street, Cambridge CB2 1PZ, UK.    Tel  : +44 223 332754
------------------------------------------------------------------------------
From: akerberg@tree.egr.uh.edu (Peeter M. Akerberg)
Subject: Question: Regarding signal processing with ANN
Keywords: signal processing, diagnostics, monitoring
Reply-To: akerberg@tree.egr.uh.edu (Peeter M. Akerberg)
Organization: University of Houston
Date: Wed, 30 Sep 1992 22:51:37 GMT


I have been looking for some time now for nets that can take "raw"  
time-series data as input. I'm particularly interested in  
applications to vibration data. I would like for the net to try to  
find appropriate features rather than finding features myself and  
then input the computed feature values to the net. Any and all  
references to papers that have attempted something like this would be  
greatly appreciated.
I would also like to know of any recent papers dealing with  
time-series prediction with ANN, particularly if they aren't yet in  
the Science Citation data base.

PLEASE email and I'll summarize. Thank you in advance for your time.
email address: akerberg@tree.egr.uh.edu

Peeter Akerberg			
Univ. of Houston 
Electrical Engineering Dept.
From: dweddin@uoft02.utoledo.edu
Newsgroups: comp.ai.neural-nets
Subject: FTP FOR ECON DATA????
Date: 2 Oct 92 11:59:34 EST
Organization: University of Toledo, Computer Services

hey,

i just thought of something...are there any FTP sights where
i can get economic data?????

mail me directly, cuz i'm not here that often.

thanks much!

don wedding
From: Edwin.Clark@mailbox.bison.mb.ca (Edwin Clark)
Newsgroups: comp.ai.neural-nets
Subject: Re: Sphere Packing
Date: 20 Sep 92 17:39:31 CST

Organization: Univ. of South Florida, Math Department

In article <1992Sep17.084115.2363@leland.Stanford.EDU> ledwards@leland.Stanford.EDU (Laurence James Edwards) writes:
>In article <1992Sep17.012439.16217@infodev.cam.ac.uk>, gjm11@cus.cam.ac.uk (G.J. McCaughan) writes:
>|> In article <716659325.29292@minster.york.ac.uk> george@minster.york.ac.uk writes:
>|> 
>|> > Does anyone know the maximum number of spheres of size H (Hamming
>|> > distance measure) that can be fitted into a N-dimensional binary
>|> > space without overlapping? References, answers or workings much
>|> > appreciated,
>|> 
>|> I'm not sure about this (it's not an area I know much about), but
>|> a couple of years ago, when I took a course on information theory
>|> and such stuff, I think we were told that the answer to this is not
>|> known.
>|> [...]
>
Your question is perhaps the fundamental problem in the 
theory of error-correcting codes. The bible on this subject
is the book

 	"The Theory of Error-Correcting Codes"
	by F. J. MacWilliams and N.J.A. Sloane

The centers of the spheres of size t in the n-dimensional
binary space are called codewords. If the spheres do not
overlap they determine a t-error correcting code. For fixed
t and n one generally wants to find as many codewords as
possible. The redundancy r of a given code is defined to be
n - log(number of codewords). The log is to base 2. See 
Appendix A in the above mentioned for some (slightly dated)
tables of the best known codes. By the way, you are correct
the answer to the question is unknown--except for a few
special values of n and t.

---Edwin Clark



--
W. Edwin Clark
Mathematics Department
University of South Florida
Tampa, FL 33620-5700

 * Origin: The Electronic Mailbox (1:348/7.0)
From: roat@rrc.chevron.com (Suzanne Roat,Chevron,(510)242-5313)
Newsgroups: comp.ai.neural-nets
Subject: Re: Fuzzy logic references wanted
Date: 1 Oct 92 21:33:21 GMT
Reply-To: roat@rrc.chevron.com
Organization: Chevron, Richmond, California

In article h2k7v@lab.ntt.jp, farzin@apollo3.apollo3.ntt.JP (Farzin Mokhtarian) writes:
>In article <1992Sep30.155618.5972@til.til.com> erik@maui.til.com (Erik Horstkotte) writes:
>
> @In article <2gf.h0hrp@lab.ntt.jp>, farzin@apollo3.apollo3.ntt.JP
> @(Farzin Mokhtarian) writes:
> @
> @|> Perhaps you are aware that the inventor (Prof. Lotfi Zadeh) is
> @|> Iranian.
> @|> Unfortunately, it seems that may have had something to do with it.
> @
> @I've seen precisely *no* evidence that prejudice against Iranians
> @has had anything to do with the slow acceptance of fuzzy logic in
> @the USA or Europe.  
>                        
>No doubt evidence of prejudice can be very difficult to produce.
>Anyhow, I said "may" so I left the door open to other possibilities.
>I would be glad if indeed prejudice against Iranians has had nothing
>to do with it.
>
> - Farzin Mokhtarian
>   farzin@apollo3.ntt.jp         
>                              


I doubt too many people knew he was Iranian until you said so.  I was
unaware of it and I've been to numerous conferences where he has been
present and the topic of conversation.

---
Suzanne Roat				Phone:	(510) 242-5313 Voice 
Chevron Research and Technology Company		(510) 242-4647 FAX
100 Chevron Way				Email:	roat@rrc.chevron.com
Richmond, CA  94802-0627
From: fzgabor@chip.ucdavis.edu (Andy Gabor)
Newsgroups: comp.ai.neural-nets
Subject: Re: Summary update: wavelet digest
Date: 29 Sep 92 20:23:42 GMT
Organization: Dept. of Neurology, University of California, Davis


This is an update to the summary recently posted describing proceedure to
subscribe to wavelet digest.  The reported address was
'wavelet@math.scarolina.edu'.  I have tried to send mail to this address
on two occasions and each time the mail was returned undelivered.  I have
requested info. from postmaster but no response yet.  Sorry!  The archives
of previously published digests is available at anonymous ftp site
'maxwell.math.scarolina.edu' in directory '/pub/wavelet/archive' (confirmed).

Will post if I get any smarter.

Andy

*******************************|
Andy Gabor                     |
Department of Neurology        |
University of California, Davis|
(email) ajgabor@ucdavis.edu    | 
*******************************|
From: jang@diva.Berkeley.EDU (Jyh-Shing Roger Jang)
Newsgroups: comp.ai.neural-nets
Subject: Re: FUZZY LOGIC & RBFNs
Date: 28 Sep 1992 20:09:31 GMT
Organization: /etc/organizaton
NNTP-Posting-Host: diva.berkeley.edu

In article <dfhz+gm@rpi.edu> sassoj@aix.rpi.edu (John J. Sasso Jr.) writes:
>  Can anyone refer me to any articles and/or books (or even your own points
>of view) that relate fuzzy logic to radial basis function networks?  Also,
>any references to multiresolution, hierarchical learning would be helpful.
>
>                                 John
>

In fact, under some minor constraints, a fuzzy inference system
can be functionally equivalent to a RBFN (radial basis function
networks). These constraints are

	1. rule no. = receptive field no.
	2. gaussian membership functions.
	3. TSK (Takagi-Sugeno_Kang) fuzzy if-then rules with nonfuzzy
           constant outputs.
	4. use of multiplication to get each rule's firing strength.
	5. same method (either weighted sum or weighted average)
	   for both models to derive their final outputs.

Under this equivalence, advances in each literatures can be 
applied to both models.  Below is a reference which I'll try to put
on Neuroprose.

@ARTICLE{JanS92itnn,
	AUTHOR = {J.-S. Roger Jang and C.-T. Sun},
	TITLE = {Functional Equivalence Between Radial Basis Function Networks and Fuzzy Inference Systems},
	JOURNAL = {IEEE Trans on Neural Networks},
	YEAR = {1992},
	NOTE = {(Forthcoming)}
}

Roger Jang
From: black@seismo.CSS.GOV (Mike Black)
Newsgroups: comp.ai.neural-nets
Subject: Question on using NASANETS in integer mode
Date: 5 Oct 92 10:51:51 GMT
Distribution: usa
Organization: Center for Seismic Studies, Arlington, VA

I'm trying to model an analog neural network using the integer mode
of NASANETS and am a little puzzled by their default setup.  The 
default sigmoid response they setup has an input scale of 0-6999
and an output scale of 0-1023.  Seems to me this would conflict
with a multi-layered neural net as the first layer would not be
able to respond to the input range of the next layer.

Has anyone else used or explored this mode of NASANETS?
Mike...
-- 
-----------------------------------------------------------------------------
 black@beno.CSS.GOV             land line: 407-676-2923  | I want a computer
 real home: Melbourne, FL       home line: 407-242-8619  | that does it all!
 CSI Inc, Computer Software Innovations, Palm Bay, FL
Newsgroups: comp.ai.neural-nets
From: erik@maui.til.com (Erik Horstkotte)
Subject: Re: Fuzzy logic references wanted
Keywords: fuzzy logic references
Organization: Togai InfraLogic, Inc.
Date: Wed, 30 Sep 1992 15:41:29 GMT

In article <4251@svin09.info.win.tue.nl>,
luit1@wsinfo11.info.win.tue.nl (Wim Jansen) writes:

|> I have to disagree with you here. Although humans do not use fuzzy
|> logic in
|> the process of *thinking*, they do use it in the process of
|> *thinking
|> about*. My point is that you are absolutely correct, if you say
|> that neural
|> networks have not much in common with fuzzy logic. However, fuzzy
|> logic is
|> a very good model for reasoning of humans.
|>  I always though that the main difference between a machine and a
|> human was
|> the accuracy with which they work. Computers calculate with
|> discrete
|> quantities, whereas humans use inaccurate (fuzzy) measurements.

I think we're probably arguing semantics here.  What I was trying to
say is, yes, fuzzy logic and fuzzy expert systems model the results
of human thinking about a certain class of problems.  I was also
trying to say that they almost certainly do not arrive at results
in the same way - that is, I seriously doubt that humans have a
MAX-MIN inferencing engine.  We have something that gives the same
*effect*, however.
-- 
Erik Horstkotte, Togai InfraLogic, Inc.
erik@til.til.com, uunet!til!erik - (714) 975-8522
Standard disclaimer: the preceding are my opinions, not TIL's.
Newsgroups: comp.ai.neural-nets,sci.physics,soc.college.grad,soc.college
From: darice@yoyo.cc.monash.edu.au (Fred Rice)
Subject: Re: Physics Depts that do Neural-Nets research
Organization: Monash University, Melb., Australia.
Date: Tue, 29 Sep 1992 14:00:14 GMT

Thanks,

I have received many replies regarding my question on physics
departments that do neural-nets research.  There are too many responses
to thank everyone individually, so I hope this group-thankyou will do.

Kind regards,

 Fred Rice
 darice@yoyo.cc.monash.edu.au   
Newsgroups: comp.ai.neural-nets,comp.ai,sci.engr.control
From: ree@utrc.utc.com (Bob Erickson)
Subject: Fuzzy logic references wanted
News-Software: VAX/VMS VNEWS 1.41    
Keywords:  fuzzy logic references
Reply-To: ree@utrc.utc.com
Organization: United Technologies Research Center
Date: Fri, 25 Sep 1992 22:23:00 GMT

I am looking for interesting papers relating to fuzzy logic.

I participate in a small group that gets together to study FL.
We are interested in different:

  uses (controls, databases, clustering, etc.)
  techniques (neuro-fuzzy, fuzzy associative memories, etc.)
  ties and comparisons to other techniques (belief, probability, etc.)

We've been through Kosko's "Neural Networks and Fuzzy Systems" book,
and about 15 papers so far.  I'd like to know which are your favorites,
with some really good insights.

Sources for references, especially network archives with papers, etc.
would especially be appreciated.  Also references to papers in journals,
proceedings, etc.  I already have access to prceedings from FUZZ-IEEE '92.

Respond by email.  I'll summarize if there are responses and interest.

Also: I've seen several postings mentioning the idea of a Fuzzy Logic
news group. (e.g. comp.fuzzy_logic or comp.ai.fuzzy_logic ...?).
Is/Was a proposal voted on?  Do you regard fuzzy_logic as part of AI?

Thanks in advance.

------------------------------------------------------------------------------
Bob Erickson                                         #include <disclaimer.std>
ree@utrc.utc.com
United Technologies Research Center
Newsgroups: comp.ai.neural-nets,comp.ai,sci.engr.control
From: emily@azariah.tamu.edu (Emily Glass)
Subject: Re: Fuzzy logic references wanted
Keywords: fuzzy logic references
Organization: Texas A&M University, Dept. of Electrical Engineering
Date: Wed, 30 Sep 1992 15:04:34 GMT

In article <1992Sep28.191711.996@til.til.com> tanaka@til.til.com (Yokichi Tanaka) writes:
>In article <25SEP199217120750@utrcv1>, ree@utrc.utc.com (Bob Erickson) writes:
>|>
>|> Also: I've seen several postings mentioning the idea of a Fuzzy Logic
>|> news group. (e.g. comp.fuzzy_logic or comp.ai.fuzzy_logic ...?).
>|> Is/Was a proposal voted on?  Do you regard fuzzy_logic as part of AI?
>|> 
>
>There has been a number of postings asking/requesting a fuzzy
>logic news group.  I've been compiling a list of people who
>have posted to "comp.ai", "comp.ai.neural-nets", or
>"sci.engr.control" regarding fuzzy since July of this year.
>I already have a list of about 70 people.
>
>I think there now is enough interest in trying to start up
>a news group.  I think with a news group of its own, the 
>traffic will pick up, too.  I am not a "net" or "news" guru,
>but I am willing to do an RFD (request for discussion) when
>I return from my trip to Japan (November 1).  If someone 
>wants to before then, go right ahead.  If you want the list of
>the e-mail addresses, let me know.
>

Yoke,

  There are 7 of us in my research group that are interested
in a fuzzy news group, plus probably others here at Texas A&M. (You 
probably already have my name, since I posted a couple of months ago, and
you asked me about interest in a fuzzy news group at that time.)

I vote for calling it comp.fuzzy.  Even though fuzzy control is "ai-ish"
in the broad sense that it is trying to mimic human reasoning, I agree 
that it should not be under the "ai" hierarchy.

Thanks for taking the initiative on this.

Emily
From: orsier@imag.imag.fr (Bruno Orsier )
Newsgroups: comp.ai.neural-nets
Subject: Request : info on books on hybrid systems
Date: 5 Oct 92 09:28:02 GMT
Organization: Institut Imag, Grenoble, France

A few weeks ago a list of books "combining the ideas behind expert
systems and neural networks" was published here :
**************************************************************************
(This list was constructed by Peter Raeth.)

Zahedi, Fratemeh, Intelligent Systems for Business, Belmont, CA:
Wadsworth, 1993

Gallant, Stephen, I., Neural Network Learning and Expert Systems,
Cambridge, MA: MIT, 1992

Blanchard, David and Paul Beard, Intelligent Applications, New
Canaan, CT:  Lionheart, 1992

Lawrence, Jeannette, Introduction to Neural Networks and Expert
Systems, Nevada City, CA: California Scientific Software, 1992
************************************************************************

Two other books may be added to this list :
Kandel A. and Langholz G., Hybrid Architectures for Intelligent Systems, CRC
Press, (date ?)

Soucek B. (ed), Neural and Intelligent System Integration, John Wiley & Sons,
new York, (date ?)

Before buying this books I would like to get more information about them.
Could somebody give advice on contents, interest, etc ...

Please email (orsier@lifia.imag.fr), I'll summarize.
-- 
----------------------------------------------------------------------------
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 
----------------------------------------------------------------------------
From: lordSnooty@cup.portal.com (Andrew - Palfreyman)
Newsgroups: comp.ai.neural-nets
Subject: AIM comparitive responses
Date: Tue,  6 Oct 92 09:23:51 PDT
Organization: The Portal System (TM)
Distribution: world

I wrote:

>Has anyone experience with the AIM program (available
>commercially) in comparison to neural net performance?
>
>Under what circumstances is either superior? What type
>of NN beats it up?

Here are the responses which I received about that...

--------------------------------------------------------------------------
Jeff Racine         jracine@weber.ucsd.edu (racine@nexus.yorku.ca):

I have used AIM and love it. Two things about the product stand out
(overall it is a great product).
 
a) Automatically discards those variables that are not contributing to
the predictive power - equivalent to significance testing

b) Yields very flexible functional networks with automatic wiring from
input to output layer e.g. if needed.

In addition it spews out C code for the estimated functional network,
allows `what if' types of questions and much much more.

I have compared the output with that from other feedforward networks
with sigmoidal nodes and multiple layers, and AIM provides
consistently better results in terms of performance on (as of training
time) unseen data from the same data generating process.

In addition, it is blindingly fast relative to using e.g. backprop to 
train a fairly unsophisticated feedforward net.

Just a satisfied customer.

--------------------------------------------------------------------------
From: belote@fubar.cs.montana.edu (David Belote)


I don't have alot of personal experience with AIM, but I can tell you what
I have been told by a friend who has used it frequently.  He says that in
all of his applications AIM trained far faster than backprop nets such as
BrainMaker and Nasa Nets.  It was able to fit 3-d curves extremely
accurately and quickly.  He is very happy with the package.

To my limited understanding, AIM is best when the number of examples is 
much more than the number of parameters.  Failing this, a neural network
approach achieves better results.

--------------------------------------------------------------------------
From: "HARRISON, TONY V" <TVH@gnv.ifas.ufl.edu>

Just some info on the product...They have an exellent demo ($20) which shows
myself) your way around it.  It also does the pre- and post-processing of your
data, and allows one to encode the polynomials into an expert system in C 
code.  Hope this helps.

-----------------------------------------------------------------------------

 --------------------------------------------------------------------------
| lord snooty @the giant |  Would You Like Space Potatoes With That?       |
| poisoned electric head |              andrew_-_palfreyman@cup.portal.com |
 --------------------------------------------------------------------------
Newsgroups: comp.ai.neural-nets
From: mgcbo@uxa.ecn.bgu.edu (Charles B. Owen)
Subject: Re: Noisy backprop- references needed
Keywords: backprop, noise, simulation, generalization, references
Organization: Educational Computing Network
Distribution: usa
Date: Tue, 6 Oct 1992 22:46:07 GMT

I am asked:  Could you explain why "white" noise is a bad choice?

At this time I cannot explain for sure why white noise is a bad choice,
though I am studying the issue.  What I have found in a large number
of experimental trials (>90,000) of various distributions and amplitudes
is that Normal distribution noise with mean of 0 and standard deviation
of 1 appears to work best.  (most reliable convergence, faster
convergence).  

Theory:  "white" noise assigns equal probability to all values within
the noise range.  Obviously, the larger value will disturb, influence
the network the most, and, when you are looking at a large number of
values (many weights) you will see a good representation of the 
distribution.  Also, note that this noise source is "clipped" at
+-wmax, so there is not the potential of escaping a "deeper" local
minima.  

Anyway, my experiments show continuous distributions work better.  
Note that, white noise comes free, and that is probably its most
valuable attribute in this application.

I am studying what the real, mathematical effect on the network of
noise insertion is at this time.  I would appreciate any comments 
along that line.

Charles B. Owen


Newsgroups: comp.ai.neural-nets,comp.ai,sci.engr.control
From: iselivl@ucunix.san.uc.edu (Victor L. Iseli)
Subject: Re: Fuzzy logic references wanted
Keywords: fuzzy logic references
Organization: University of Cincinnati
Date: Thu, 1 Oct 1992 14:12:52 GMT


There are at least 5 people in my lab who read 'comp.ai' but who also have
an interest in fuzzy.  Please add us to the list of wannabe fuzzy news
readers.

I also agree with Emily.  Heavy traffic shouldn't be the number one criterion
for deciding on whether a newsgroup should be started, especially since people
have been persistently requesting such a newsgroup for a significant length of 
time.

--Victor Iseli.
iselivl@ucunix.san.uc.edu

  ----------------------------------------------------------------------
...Who dey think dey gonna beat dem Bengals?  ...Nobody dis weeken' 'cause
we ain't playin'; we gotta bye (It's a darn good thing, too, 'cause we suck).
  ----------------------------------------------------------------------
Newsgroups: comp.ai.neural-nets