HDTenEightyP/NetNews huggingface dataset:
From: aw3@ukc.ac.uk (Alan Wilson)
Newsgroups: comp.ai.neural-nets
Subject: SImple pattern recognition problem - any answers?
Date: 23 Jul 92 10:09:18 GMT
Organization: Chemical Laboratory, University of Kent at Canterbury, CT2 7NH, UK

Hello everyone in net-land. I hope some bright sparks can help me with
a simple interpretation problem (I have been asked to process this data
on behalf of a friend).

The Background: 45 questions were asked of two groups of people (5 each
of A and B) Each question had six possible responses of which one was 
recorded and given a score (1-6). A small data set, but I thought PR
may be able to do something with it.....

The Data: A data matrix was set-up and the principal components 
extracted. A plot of PC1 (x-axis) vs PC2 (y-axis) is given below


                                     |
                                     | A
                                     |
                                     |
                                     |
                                     |
                                     |       A
                                     |
                                     |
                                     |           A
                                     |
          --------------------------------B------------------------
                                  B  |
                         B           |
           B      B                  |
                                     |             A
                                     |
                                     |
                                     |
                                     |
                                     |
                                     |                   A
                                     |

The QUESTION: What does this say about the data? In particular, can one
infer any correlation between the responses of the two groups of people?

Any suggestions, etc... please post or e-mail.

Thanks in advance

ps I have numbers for eigenvalues, eigenvectors etc.

--
Alan Wilson        (insert YOUR favourite witty remark..........)

"You would NOT believe what has been going on behind there" 
================================================================= 
Newsgroups: comp.ai.neural-nets
From: andrewh@metro (Andrew Hunt)
Subject: Re: comp.ai.pattern-recognition (was: Speech recognition and Time Warping)
Organization: Sydney University Computing Service, Sydney, NSW, Australia
Date: Sun, 26 Jul 1992 22:49:07 GMT

From article <1992Jul21.213334.21975@Csli.Stanford.EDU>, by kawai@Csli.Stanford.EDU (goh kawai - n6uok):
> 
> I am a linguist (a phonetician, to be precise) working on speech
> recognition.  I would be very interested in starting a news group, either
> for pattern recognition in general or for speech processing in particular.
> My colleagues and I recently established a speech processing newsgroup in
> Japan -- fj.comp.speech -- and would like to see something similar here on
> Usenet.  
> 

I support this fully.  Since there is a growing interest in both real-
world speech applications and speech recognition/synthesis/analysis
research there must be quite a few people interested.

Perhaps comp.speech or comp.speech.research.  Currently the speech
technology and research stuff is spread across at least 5 relevant
groups.  Anyone else keen?  What is the protocol?

Andrew Hunt
andrewh@ee.su.oz.au
From: own@castle.ed.ac.uk (O Morgan)
Newsgroups: comp.ai.neural-nets
Subject: Re: Rule extraction from NN
Date: 27 Jul 92 10:53:18 GMT
Organization: Scottish Agricultural College

In article <25230@sophia.inria.fr> weigl@sibelius.inria.fr (Konrad Weigl) writes:
>There was a paper in the Washington conference IJCNN '90 about
>rule extraction from a backprop network that had learn the optimum
>opening bid for bridge;
>it was a poster, and done by two frenchmen, of which one was by 
>CEMAGREF; I am sorry, that is all I know.
>
These are:
Bochereau L. and Bourgine P.  "Extraction of semantic features and
logical rules from a multilayer neural network" Proceedings of the IJCNN
90, Washington DC 15-19/01/90

Bochereau L.  and Bourgine P.  "Validity domain and extraction of rules
from a multilayer neural network" Proceedings of the IJCNN 90,
Sand Diego 17-21/06/90

Olly Morgan
--
----------------------------------------------------------------------------
 Olly Morgan @ Scottish Agricultural College,  Edinburgh EH9 3JG, Scotland
               Tel: (+44 31) 667 1041          E.Mail: O.Morgan@ed.ac.uk
----------------------------------------------------------------------------
Newsgroups: comp.ai.neural-nets
From: chan@uceng.UC.EDU (Alistair Keating Chan)
Subject: Neural Net for Matlab
Organization: College of Engineering, University of Cincinnati
Date: Wed, 22 Jul 1992 13:47:59 GMT


Has anyone seen or written a standard backpropgation algorithm
for Matlab that they would like to and be able to share?

Please e-mail.

Thanks in advance.

			chan@uceng.uc.edu
From: demers@cs.ucsd.edu (David DeMers)
Newsgroups: comp.ai.neural-nets
Subject: Re: papers or books on Bayes Classifier
Date: 25 Jul 92 23:02:23 GMT
Organization: =CSE Dept., U.C. San Diego

In article <batra.712042532@beagle> batra@boulder.Colorado.EDU (sajeev batra) writes:

>I'm interested in learning some more about this Bayesian classifier.
>Could someone suggest to me some introductory level papers that either
>use or explain the use of Bayesian classifiers in pattern recognition.
>Or an introductory level book would also be fine.

There is a lot of literature in many different fields.  I recommend
Duda & Hart, [don't have it with me, but title is something like:]
Pattern Recognition and Scene Analysis.  It's from about 1973, but
quite current (!) and very well written.  A good place to start.

-- 
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
From: henrik@mpci.llnl.gov (Henrik Klagges)
Newsgroups: comp.ai.neural-nets
Subject: Re: Back-percolation
Date: 30 Jul 92 18:01:01 GMT

jondarr@macadam.mqcs.mq.oz.au (jondarr c g h 2 gibb) writes:
>tell me why the interest seems to have died out ?

It's not mathematically sound and basically a collection of a few
foggy pseudoheuristics bolted upon backpropagation which work only
on Sunday afternoons.

Cheers, Henrik
IBM Research Division physics group Munich
massively parallel group at Lawrence Livermore

Newsgroups: comp.ai.neural-nets
From: sareibi@cheetah.vlsi.waterloo.edu (Shawki Areibi)
Subject: e-mail address of Carsten Peterson
Organization: University of Waterloo
Date: Thu, 30 Jul 1992 20:26:05 GMT

hi, does anyone out there know the e-mail address of "Carsten Peterson"
at the Departement of Theoretical Physics, University of Lund Solvegatan , Sweden.

Newsgroups: comp.ai.neural-nets
From: fs0997@easttexas.tamu.edu (Frank Schnorrenberg 5-9403)
Subject: Neocognitron: ftp address
Summary: archive location
Organization: Computer Science Dept., Texas A&M University
Date: Tue, 21 Jul 1992 14:57:31 GMT


There is an implementation of the simulator AVAILABLE at the following
location using anonymous ftp:

                128.194.15.32 alias tamsun.tamu.edu
                Filename: /pub/neocognitron.Z.tar

I hope this clears the confusion my mail server created by not 
sending notification messages to all the people interested.

If you have comments etc., send e-mail to schnob@cs.tamu.edu.


The simulator is written in C and comes with a list of references 
which are necessary to read to understand the specifics of my
implementation. I coded the unsupervised version without (!)
C-cell inhibition.

Take a look and if you like it- fine, if not, remember: 
It was free!

		
--
Frank Schnorrenberg				e-mail:	schnob@cs.tamu.edu
Department of Computer Science
Texas A&M University
College Station, Texas
From: axh@cs.bham.ac.uk (Alex Hooper)
Newsgroups: comp.ai.neural-nets
Subject: ftp sites for tech reports
Keywords: cascor
Date: 27 Jul 92 13:37:51 GMT
Organization: University of Birmingham


	I am looking for a site where I can ftp a copy of 
the tech report CMU-CS-90-100. This is a Carnegie Mellon 
University tech report on the cascade correlation
learning architecture.

	Please email axh@cs.bham.ac.uk

	Any help greatly apreciated since the British
Library can't seem to get their hands on it.


____________________________________________
Its long been a hobby of mine
A new value for PI to assign
I'd set it at three
For its easier you see
Than 3.14159...
                   (Alexander Hooper)
                   (axh@cs.bham.uk.ac)
____________________________________________
From: tedwards@src.umd.edu (Thomas Grant Edwards)
Newsgroups: comp.ai.neural-nets
Subject: Re: ALN vs BP
Date: 24 Jul 92 19:41:22 GMT
Organization: Systems Research Center, Maryversity of Uniland, College Park

In article <1992Jul24.053623.22636@cs.yale.edu> tsioutsias-dimitris@CS.YALE.EDU (Dimitris Tsioutsias) writes:
>It seems that after the backprop fans, we have now the ALN ones. Why
>each group (or any other that shows strong support) is trying to pass
>its nets as the dominant ones?

Yeah, but there are alot of people who want to throw nets at real problems
today, and it would be a little silly for anyone to expect gradient 
descent MLP nets at any real problem and expect results.

Clearly, ALN's do the job (learning) real fast.  So do locally receptive
fields (infact, if you have a good grasp on the chaotic dynamics of
the problem, using self-adaptive receptive fields is a big win).
Still, even a good conjugate-gradient MLP program should work OK, but I
don't feel like writing one!  Cascade-Correlation is particularly neat,
especially for really difficult problems.

The thing to remember is that NEURAL NETS DO NOT PERFORM MIRACLES.
They will not predict the stock market well, nor will they predict daily
solar flux from the sun months in advance, because there is too much
missing information besides whatever input you give the net to ever solve
the problem.  They are best used in situations where a person, if he/she
sat down for a few weeks and worked on the problem given the input you
give the net, and figure it out.  Sonar problems already solved by
sonarmen, truck backing up problems already figured out by truck drivers,
etc.  They are best utilized as quick-learning first-order solutions to
problems.

Of course, there is plenty of machine-learning theory to be worked
out from neural nets, but that is research, not application...

-Thomas Edwards
Newsgroups: comp.ai.neural-nets
From: venkates@oak.egr.uh.edu (Ramchandar R. Venkatesan)
Subject: Re: Fuzzy Logic and ANN's
Organization: University of Houston
Date: Fri, 24 Jul 1992 04:33:54 GMT

In article <jasonb.711686076@woylie> jasonb@cs.uwa.oz.au (Jason S Birch)  
writes:
> Hi.
> 
> I am interested in any research that may have been done in
> combining Fuzzy Logic Controllers and ANN's. Also, what
> efforts have been made in training FLC's? References, ideas,
> comments would all be greatly appreciated.
> 
> Thanks in advance,
> Jason.
> jasonb@cs.uwa.edu.au

The book "Neural Networks anf Fuzzy Systems " by BART KOSKO is a good book  
to begin with...
Bye 
Ram.
Newsgroups: comp.ai.neural-nets
From: arms@cs.UAlberta.CA (Bill Armstrong)
Subject: Re: Thanks Neural Nets, and Brains?
Organization: University of Alberta, Edmonton, Canada
Date: Thu, 23 Jul 1992 23:01:23 GMT

connolly@rabbit.cs.umass.edu (Christopher Ian Connolly) writes:

>In article <arms.711907358@spedden> arms@cs.UAlberta.CA (Bill Armstrong) writes:
>>First off, isn't it rather strange that the most widespread artificial
>>model of neural operation. the multilayer perceptron, uses continuous
>>quantities on its connections, while the dendrites and axons of
>>neurons use "zero or one" type action potentials?

>Don't forget that electrical synapses (gap junctions) are an analog
>means of intercellular communication.  Whether they represent a
>significant part of brain function is another question entirely.
>-- 

Of course, and in adaptive logic networks (ALNs), if you want to deal with
continuous signals, you need to have input conversions which generate
booleans: for example thresholds x>= const.

--
***************************************************
Prof. William W. Armstrong, Computing Science Dept.
University of Alberta; Edmonton, Alberta, Canada T6G 2H1
arms@cs.ualberta.ca Tel(403)492 2374 FAX 492 1071
Newsgroups: comp.ai.neural-nets
From: ak45ldp@Texaco.com (Larry D. Pyeatt)
Newsgroups: comp.ai.neural-nets
Subject: Re: Back Propogation - ??????
Keywords: Learning - Strange
Date: 29 Jul 92 13:50:42 GMT
Organization: Texaco

In article <1992Jul28.170531.3687@morgan.ucs.mun.ca>, raghu@morgan.ucs.mun.ca (Raghu B) writes:
  ....
|>        I always find a bowl shaped nature in the neural behaviour.
|> The error is at an acceptable level. Is bowl shape  a  characteristic
|> of bp algorithm ? I am curious to know more about it.
|> 

The NN should have been able to fit your data much better than
your graph indicates.  It is possible that you have too many
neurons in your hidden layer, or that you do not have enough training
data, or that you need to train for a longer period of time.

You may notice some small nonlinearities in the NN response curve,
but not on the order that your graph suggests.

-- 
Larry D. Pyeatt                 The views expressed here are not
Internet : pyeatt@texaco.com    those of my employer or of anyone
Voice    : (713) 975-4056       that I know of with the possible
                                exception of myself.
From: inde47d@jane.uh.edu (Shivakumar, Vaithyanathan)
Subject: References needed desperately!!
News-Software: VAX/VMS VNEWS 1.41    
Organization: University of Houston
Date: Wed, 22 Jul 1992 06:55:00 GMT

Hi !

I need some urgent help in obtaining copies of the following references. Of
course, I will pay for any cost that is incurred. 

1. Chiu, C., C-Y, Maa & M.A. Shanblatt - "AN artificial neural network algorithm
for Dynamic Programming", International Journal of Neural Systems, Vol 1, No 3, 
pp 211-220, 1990.

2. Lee, H-M. & C-H.Hsu "Neural Network Processing through Energy Minimization
with learning ability to the multi-constraint zero-one knapsack problem" 
Proceedings of Tools for AI conference, Fairfax Virginia, USA, 1990.

Thank you very much in advance -

Shivakumar Vaithyanathan
Dept. of Industrial Engineering
University of Houston
Houston, Tx - 77204-4812

fax	: (713) 743-4190
ph	: (713) 743-4186
From: clarke@acme.ucf.edu (Thomas Clarke)
Newsgroups: comp.ai.neural-nets
Subject: Re: Neural Nets and Brains
Date: 24 Jul 92 16:45:44 GMT
Organization: University of Central Florida

In article <arms.711986585@spedden> arms@cs.UAlberta.CA (Bill Armstrong)  
writes:
> ....  My point
> was that BP nets use continuous signals and the brain doesn't -- an
> obvious very significant difference.  I was asking why people would
> expect to understand the brain by studying a system (BP) that is
> *different* at the most basic level of signalling.
> 
> Don't you agree that if the brain works on 0-1 signals, then to study
> the brain one could beneficially look at logical systems? 

I believe the brain may well use continuous or analog signals.

While individual neuron action potentials are on or off, they 
often occcur in trains of varying frequency.  The time-density of 
action potentials then forms an analog or continuous variable 
that may be processed in much the same way as a BP network.  

In fact, if you posit a central clock in the brain, the phase of 
the action potentials with respect to the clock provide another 
possibility for a continuous variable.  Indeed, both phase and 
density could work together to make the brain a 
complex-valued neural network :-)  
--
Thomas Clarke
Institute for Simulation and Training, University of Central FL
12424 Research Parkway, Suite 300, Orlando, FL 32826
(407)658-5030, FAX: (407)658-5059, clarke@acme.ucf.edu
From: erwin@trwacs.fp.trw.com (Harry Erwin)
Newsgroups: comp.ai.neural-nets
Subject: Re: Neural Nets and Brains
Date: 26 Jul 92 13:38:50 GMT
Organization: TRW Systems Division, Fairfax VA

>Exercise:  Give a precise, mathematical definition of the "rate of
>fire" of a neuron.  It's a lot harder than it looks!  In fact, as far
>as I can tell, there *is* no perfectly good definition (though there
>are ones good enough to be useful).

Given that the "rate of fire" of a neuron is the object of interest (in
fact, given that the modulation of the rate of fire is what's specifically
of interest, since the dynamics of the neuron rapidly converge to a stable
state), I would suspect that neurons use wavelet filters to determine if
the signals they're seeing contain information. 

Cheers,
-- 
Harry Erwin
Internet: erwin@trwacs.fp.trw.com

Newsgroups: comp.ai.neural-nets
From: raghu@morgan.ucs.mun.ca (Raghu B)
Subject: Back Propogation - ??????
Keywords: Learning - Strange
Organization: Memorial University of Newfoundland
Date: Tue, 28 Jul 1992 17:05:31 GMT

	Thanx for the response for my earlier query. I found another
strange behaviour in my bp algorithm. I have  in my plot of
actual and neural values something like this always.


	:
	:            :      /  
	:              :   /  :
	:               : / :
	:               / :          :  neural  values
        :             /              /   actual values 
        :
        :
        :
        : 
        :------------------------------------>
	          time --->

       I always find a bowl shaped nature in the neural behaviour.
The error is at an acceptable level. Is bowl shape  a  characteristic
of bp algorithm ? I am curious to know more about it.

	Waiting for positive responses from dear netters.

				Raghu,Balasubramanian
From: hcbarth@afterlife.ncsc.mil (Bart Bartholomew)
Newsgroups: comp.ai.neural-nets
Subject: Re: need for unique test sets
Date: 22 Jul 92 03:36:33 GMT
Organization: The Great Beyond


	Prof Armstrong and I are converging on agreement.
In particular, his last two points 
a. without a priori knowledge, no one can know what the "correct"
   function is, based on a fixed finite sample.
and
b. in general, no set of tests unless they cover the whole space
   can assure that the neural net output will do what is correct
   even if you know what "correct" means.  There has to be a proof
   technique somehow.
These remarks are clearly correct.  In practice, if you don't/can't
know for sure that you have the correct answer, then the best you 
can do is try to tilt the odds in your favor.  The more data you have
available the better, if you have the compute power to work with 
it all.  If there are (as I suspect) an infinite number of functions
that can generate any data set, then you have no guarantee of 
selecting the 'right' one no matter what you do.  All you can
do is hope that you have at least found an equivalent function.
	Then you pays your money and you takes your chances.
There are no guarantees in life.  Even real-life pilots make
mistakes and drop people out of the air.
	Bart
-- 
"It's not the thing you fling, the fling's the thing." - Chris Stevens
 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
From: sef@sef-pmax.slisp.cs.cmu.edu
Newsgroups: comp.ai.neural-nets
Subject: Re: Rule extraction from NN
Date: 27 Jul 92 14:13:56 GMT
Organization: School of Computer Science, Carnegie Mellon


The general problem with rule extraction from neural nets is not solvable.
A neural net provides a richer language than can be captured in a small,
human-readable set of rules, whether symbolic or fuzzy.  Sometimes you can
look at a trained net and say something like, "The output is the same as
input B unless both of C and D are present."  But more often you're faced
with, "The output is 3.257 times the sigmoid of B unless C is more than
457 times the sigmoid of .698 D plus E time 1.035e-11..."  Even if you
translate into the language of fuzzy rules ("much bigger than" and so on),
the latter kind of expression is going to be ugly.

In such cases, you can either give a clean, simple rule that is a crude
approximation to what the network is really doing (but perhaps sufficient
for your needs), or can extract an expression that gives the whole truth,
but that is not human-readable in any useful sense.  Shavlik and others
have claimed that in certain restricted domains, with certain restricted
architectures, almost all the nets produced are easily convertible into
reasonably simple rules.  Personally, I suspect that you can only count on
this if the restrictions are very severe.

Looked at from the other side, this is a kind of advantage: neural nets
allow us to explore the wide universe of mappings for which there is no
simple rule-based equivalent.

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

Internet: sef+@cs.cmu.edu
From: vg@wave.cis.ufl.edu (V. Girish)
Newsgroups: comp.ai.neural-nets
Subject: time series forecasting neural network
Date: 22 Jul 92 17:43:07 GMT
Organization: Univ. of Florida CIS Dept.

Hi,
I am looking for a time series forecasting neural network in the
public domain . I would appreciate if somebody can mail me the
place from where I can ftp the stuff .
Thanks a lot

Girish

  Home:-                               Office:-
  1112 S.W 16 th Ave.,#112,                Center for Computer Vision Research,
  Gainesville, Fl-32601.                  E333,Department of CIS,UF,Gainesville.
  Ph.# (904)-377-2004.                      Ph.# (904)-392-1435.
                                          E404,Department of CIS,UF,Gainesville
                                                 (904)-392-2585.
From: esran@warwick.ac.uk (Mr P H Chan)
Newsgroups: comp.ai.neural-nets
Subject: Right size for each hidden layer
Date: 22 Jul 92 15:46:26 GMT
Organization: Computing Services, Warwick University, UK

Hollo,

I am working on applying multi-layer perception network to
pattern recognition area. One of my problems is how to determine
the right size for each hidden layer.
Can anyone give me some reference concerned with this problem ?

Thank in advance


P. H. Chan

-- 
------------
Department of Engineering	|	E-mail :
University of Warwick		|	es283@uk.ac.warwick.eng
Coventry U.K.  CV4 7AL		|	esran@warwick.ac.uk
Newsgroups: comp.ai.neural-nets
From: gsbg9079@uxa.cso.uiuc.edu (G. Scott Braley)
Subject: Adam Blum Email address?
Organization: University of Illinois at Urbana
Date: Fri, 31 Jul 1992 19:28:28 GMT

I am looking for an Email address for Adam Blum, author of Neural Networks
in C++.  Any help would, of course, be appreciated.

			-Scott

-- 
*****************************************************************************
*  F.B.I.B.M.--The most feared merger  *  gsbg9079@uxa.cso.uiuc.edu         *
*****************************************************************************

Newsgroups: comp.ai.neural-nets
From: vsharma@gmuvax2.gmu.edu (Vin Sharma)
Subject: Re: Training Networks on Chaotic Time Series
Keywords: chaos within
Organization: George Mason University, Fairfax, Va.
Date: Thu, 23 Jul 1992 18:26:18 GMT


Would it be possible to design the net itself as a chaotic dynamical
system? I am interested in the possibility of chaos within, serving as
a representation space: either the evolution of the net as a chaotic
system or the presence of a chaotic pattern within the net.Suggestions?

Vin
---


-- 
                  ---------------------------- 
                  Confusion will be my epitaph
		  ----------------------------