HDTenEightyP/NetNews huggingface dataset:
From: dld@magi.ncsl.nist.gov (Darrin Dimmick X4147)
Newsgroups: comp.ai.neural-nets
Subject: fl3 distribution corrected
Date: 13 Oct 92 17:12:19 GMT
Organization: National Institute of Standards and Technology, Gaithersburg, MD

National Institute of Standards and Technology

It has been brought to my attention that some of the character images
contained in the fl3 distribution of isolated hand-print digit images,
available via anonymous ftp, were compressed contrary to my previous posting.

I have decompressed these images and recreated the tar file containing the fl3
distribution. This corrected fl3 distribution, also named fl3.tar.Z, has 
replaced the previous distribution on the ftp site and can be obtained using 
the instructions found in the original posting.

If you obtained the fl3 distribution prior to seeing this posting I recommend 
obtaining the updated distribution.

I apologize for any inconvenience this error may have caused.   

-- 
Darrin L. Dimmick
National Institute of Standards and Technology
Bldg. 225/A216
Gaithersburg, MD. 20899
Newsgroups: comp.ai.neural-nets,sci.engr.control
From: tanaka@til.til.com (Yokichi Tanaka)
Subject: Re: Fuzzy System Variable Independence
Organization: Togai InfraLogic, Inc.
Date: Fri, 9 Oct 1992 00:32:28 GMT

In article <1992Oct7.154200.21346@til.til.com>, erik@maui.til.com (Erik Horstkotte) writes:
|> In article <1992Oct6.174755.9360@cs.ucla.edu>, cooper@oahu.cs.ucla.edu
|> (Mark G. Cooper) writes:
|> 
|> |> Do the inputs to a fuzzy logic system (e.g. a fuzzy associative
|> |> memory)
|> |> have to be independent or are there any situations where they have
|> |> to be
|> |> independent?
|> 
|> There is nothing I've seen that indicates that they have to be
|> independent.  In fact, I've done several fuzzy systems where the
|> inputs were partially interdependent.
|> -- 
|> Erik Horstkotte

I, too, don't know of any situations where input variables *have*
to be independent.  As Erik stated, there are many cases where
the variables are interdependent.  In control applications,
for instance, one may use x="error in distance" and dx/dt="rate of
change of error" as inputs.  (I don't think x and dx/dt would
be considered independent in this case.)

How about another scenario ... you have inputs x, y and z.
z = x + y.  v,w are your outputs.  In this case, z is definitely
not an independent variable.  However, by having z, it may
simplify rule generation and may improve performance.  Suppose
you had 2 rules (hmm, that's usually not enough, but ...):
    PB = positive big
    PS = positive small
    ZERO = zero
    NS = negative small
    NB = negative big

  Rule 1:   If x is PB and y is PB, then v is NB
  Rule 2:   If x+y is ZERO,         then w is PB

Now, if you had z=x+y as an input, you only need 1 rule

  Rule 2:   If z is ZERO,           then w is PB

But if you didn't, you may need a minimum of 3 more rules

  Rule 2a:  If x is PB and y is NB, then w is PB
  Rule 2b:  If x is PS and y is NS, then w is PB
  Rule 2c:  If x is Z  and y is Z,  then w is PB

And what about cases like "x is PB and y is NS", etc?

So in this case, it seems like having this dependent
variable z would simplify rule generation, and most
likely improve performance.

Real world applicability?

  x = temp deviation from 72 degrees F for room 1
  y = temp deviation from 72 degrees F for room 2
  v = air conditioner which serves both rooms 1 and 2
  w = opening of a vent duct between rooms 1 and 2
  (see if x+y is near 0, you can save AC costs by opening
   the vent duct, instead of turning on the AC)

  If temp in room 1 is 80 degrees and temp in room 2 is
  64 degrees (x=+8, y=-8, z=0) ... then just open the 
  vent duct.  (I'm sure it's not that simple, but ...)
-- 
Yoke (Yokichi) Tanaka      Togai InfraLogic, Inc.    "fuzzy"
tanaka@til.com             Irvine, California, USA
uunet!til!tanaka           714-975-8522
In case you're wondering ... I am male.
"The more you drive, the less intelligent you are."
Newsgroups: comp.ai.neural-nets,comp.ai,sci.engr.control
From: erik@maui.til.com (Erik Horstkotte)
Subject: Re: Fuzzy logic references wanted
Organization: Togai InfraLogic, Inc.
Date: Fri, 9 Oct 1992 15:56:22 GMT

In article <JMORRIS.92Oct8100944@mean.mu.caltech.edu>,
jmorris@hot.caltech.edu (John Morris) writes:

|> Has anyone begun to consider MIMO implications of fuzzy systems? I
|> have
|> spoken with quite a few fuzzy theorists and the current trend is
|> to
|> either use tons of cross-mappings (which makes the system very
|> computationally unattractive) or treat the problem as a bunch of
|> SISO
|> problems (which I hope you see is not a good thing to do). Both of
|> which are also quite ad hoc.

I haven't heard of anyone who is working on this particular problem
from a theoretical point of view, but I have seen a very few fuzzy
control systems actually implemented for such situations.  The vast
majority of fuzzy control systems are of the "multiple derivatives
and integrals of one input in, one output" sort.

I don't quite understand the need for the cross-mappings.  Perhaps
these were introduced to solve some problem in the analysis rather
than in the actual controller.  The few fuzzy control systems of
this kind that I've been involved with have all been of the ad-hoc
sort, and none have done anything but very straightforward control
rules.  Perhaps I don't understand what you mean by cross-mappings.

Of course, some of this is moot anyway, because I have yet to see
a real stability theory for fuzzy control, even in simpler cases.
-- 
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.
From: ck621@cleveland.Freenet.Edu (Ralph Cherubini)
Newsgroups: comp.ai.neural-nets
Subject: Some fuzziness regarding identification, error, and art
Date: 14 Oct 92 12:34:29 GMT
Article-I.D.: usenet.1bh44lINNnhp
Organization: Case Western Reserve University, Cleveland, Ohio (USA)
NNTP-Posting-Host: slc4.ins.cwru.edu


                               That for certain
 
I was close upon the ocean waves
when they turned mountainous and hard
leaving me to imagine how I had moved
suddenly from water to land
mistaken
I saw that I was lying in the hair
of a giantess' head
it was not the sea
nor the mountains either
which held me waving wildly
anyway above
that for certain.
 
----------
A poem about misinterpretation
about misassignment
about metaphor
as a blurring of categories
and this
which you read now
is a gloss
on the distinctions among:
correct seeing and distinctiveness
blurred identification
hallucination
 
The prize
of comprehension
goes to hir
who can see how artificial neural networks
can:
illustrate
exemplify
model
the transitions from:
     identification to
     misidentification
 
     literalism to
     metaphor.
 
One man's error is
another woman's art.
 
One can see
where this will inevitably
commercially
lead:
By 1995
some firm
some where
will market a subsystem
which will scan a photograph or drawing
and generate as output
a version of the scene
a la Picasso
Matisse
your four-year old child
From: William.Stevenson@p0.f20.n7101.z5.fidonet.org (William Stevenson)
Newsgroups: comp.ai.neural-nets
Subject: Artificial Logic Networks and CMAC
Date: 5 Oct 92 09:23:00 GMT
Article-I.D.: catpe.2540.2AD1D737
Organization: FidoNet node 5:7101/20.0 - Unisa Editorial, Pretoria


Hi!

Are there any links or similarities between Albus's Cerebellum Model Artic
lation Controllers and Artificial Logic Networks?  Both seem to expand the
feature space into a large number of bit patterns, but CMAC via a random
hashing function; both claim to be fast learners.  What other
similarities are there, and are they meaningful?


William


--  
INTERNET: William.Stevenson@p0.f20.n7101.z5.fidonet.org
via:  THE CATALYST BBS in Port Elizabeth, South Africa.
       (catpe.alt.za)   +27-41-34-1122 HST or +27-41-34-2859, V32bis & HST.
From: shtmar04@uctvax.uct.ac.za
Newsgroups: comp.ai.neural-nets
Subject: Voice PITCH recognising net...
Date: 9 Oct 92 21:16:32 +0200
Keywords: neural net voice pitch music
Organization: University of Cape Town

Hi netters.

Anybody have any suggestions for getting a net to recognise the
*pitch* of a human voice  (not interested in voice recognition).

I could have the voice digitised - possible fourier/hartley transform or powers
series...

I need to know:
	1) what sort of net,
	2) has the wheel been invented?
	3) if so,  can it roll this way?!

Thanks,
Mark Shuttleworth

Xref: sparky comp.ai.neural-nets:3847 ont.events:468 can.ai:44
From: apr@cbnewsl.cb.att.com (anthony.p.russo)
Subject: Formula relating gneralization confidence to dataset size
Organization: AT&T Bell Laboratories
Distribution: usa
Date: Fri, 9 Oct 1992 21:25:45 GMT
Keywords: Baum/Haussler, VC dimension, confidence interval, capacity


Hi again.

I am looking for a formula (and references!) with which I can
determine the number of exemplars needed to be confident with
some probability P that my neural net has generalized to some
some performance G, given that I have trained it to some error E.

In other words, I have a network of known size. How many
patterns do I need to train it and test it, given that I
need to gneralize to some performance goal, and that I can train it
to some small error?

I know about the Baum/Haussler paper, but the formula there
gives an upper bound that is not very tight. I was wondering
if, since then, other approaches or research have tightened
the bound.

Rules of thumb are ok if references are provided; recent
results making use of VC dimensionality are favored.

Thanks in advance (and thanks to all who help with my noisy-BP request).

Tony Russo

	~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
	~  	 Tony Russo		" Surrender to the void."	~
	~  AT&T Bell Laboratories					~
	~   apr@cbnewsl.att.com OR apr1@homxc.att.com			~
	~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-- 

	~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
	~  	 Tony Russo		" Surrender to the void."	~
	~  AT&T Bell Laboratories					~
From: mwkoch@sandia.gov (Mark W Koch)
Newsgroups: comp.ai.neural-nets
Subject: Help with BCM: Unsupervised Feature Extraction
Date: 15 Oct 92 23:03:26 GMT
Distribution: usa
Organization: Sandia National Laboratories

I've been trying to use the BCM network for unsupervised feature extraction
without much success.  Any hints or ideas would be greatly appreciated.

The references I've been using are:

  1) Intrator and Cooper, "Objective function formulation of the BCM theory
  of visual cortical plasticity: statistical connections, stability 
  conditions,"  Neural Networks, Vol. 5, pp 3-17, 1992.

  2) Intrator, "Feature extraction using an unsupervised neural network,"
  Neural Computation, 4, pp 98-107, 1992.

  3) Intrator, "Feature extraction using an unsupervised neural network,"
  Proceedings of the 1990 Connectionist Models Summer School, pp. 310-318, 1990.

The basic equations for a single neuron output system (I plan to go to
multiple outputs once I can get single one to work) are:

  X=[x1,...,xn,1]      {the input vector}.
  M=[m1,...,mn,b]      {the weight vector}
  where b=the bias.

  c=sigmoid(M dot X)      {the activation}
  where sigmoid(x)=1.0/(1.0+exp(-1.0*x))

  dM/dt=u*E{phi(c,tm)*sigmoid'(c)*X}      {the learning law}
  where sigmoid'(c)=c*(1.0-c)      {the derivative of the sigmoid}
        E{U}=the expected value of U.
        u=learning rate
        phi=c^2 - c*tm
        tm=E{c^2}.

The idea, as I understand it, is that most projections of an input space
have a Gaussian distribution.  One can think of M dot X as a projection of 
the input vectors onto a one dimensional space.  The interesting projections 
are ones that are far from Gaussian, for example bi-modal.

The BCM learning law likes distributions where c is close to zero or
larger than tm.  The variable tm is dynamic and depends on c. According to the
above references: it can move to positions such that the distribution of
c will never be concentrated on one of its sides.  Unfortunately this
isn't true for me.  I start out with small random weights which gives an
initial distribution of c at 0.5 with a small variance.  Since
tm=E{c^2}=variance+mean^2 and the variance is small, tm is approximately
the mean^2 or 0.25.  This makes the distribution of c on one side of
tm and the algorithm is very "happy" at keeping things the way they are.

I've tried tm=E{c} which works a lot better, but it violates the basic
assumptions of the paper.  It seems like I need to initialize the weights 
a different way or maybe use a different variation of the sigmoid.
Any ideas?

Please reply to mwkoch@somnet.sandia.gov and I will summarize the 
replies to the net.

Thanks!
From: bnns93@cs.bham.ac.uk (British Neural Network Society)
Newsgroups: comp.ai.neural-nets
Subject: Call For Papers
Summary: Symposium wants your submissions
Keywords: cfp
Date: 16 Oct 92 16:15:22 GMT
Followup-To: bnns93@cs.bham.as.uk
Organization: School of Computer Science, University of Birmingham, UK


			CALL FOR PAPERS
		British Neural Network Society

			Symposium on

		Recent Advances in Neural Networks

Wednesday 3rd February 1993

Lucas Institute
University of Birmingham
UK


Contributions are invited for oral and poster presentations.  Topics of
interest include, but are not restricted to, the following areas: 

- Theory & Algorithms   Time series, learning theory, fast algorithms.

- Applications          Finance, image processing, medical, control.

- Implementations       Software, hardware, optoelectronics.

- Biological Networks   Perception, motor control, representation.

The proceedings will be published as a book after the symposium.


Please send a one-page summary, postmarked by 30th November 1992, to:

        BNNS'93
        c/o Russell Beale
        School of Computer Science
        University of Birmingham
        Edgbaston
        Birmingham  B15 2TT
        United Kingdom
        Tel:    +44 (0)21 414 4773
        Fax:    +44 (0)21 414 4281
From: art@playfair.Stanford.EDU (Art Owen)
Subject: Neural Nets and Blackjack
Organization: Stanford University
Date: Wed, 14 Oct 92 18:32:50 GMT

Has anybody trained a neural net to play blackjack?
If so, did it learn to count cards?  Did it count
more effectively than the published schemes?

[I don't expect casinos would let you actually use
an NN containing device.]

Art Owen
Dept Statistics
Stanford Univ
From: craiger@nprdc.navy.mil (J. Philip Craiger)
Newsgroups: comp.ai.neural-nets
Subject: In search of critiques of NN software
Date: 7 Oct 92 16:46:55 GMT
Distribution: usa
Organization: Navy Personnel R&D Center


I will soon be conducting some research that requires the use of NN
software.  I would like to ask those of you who have used the various
NN packages how well you liked each of the packages, and what you
feel are the advantages and disadvantages of each.

I can be reached at:  craiger@nprdc.navy.mil.

I thank you in advance for your assistance.


Philip Craiger
From: osm8502@zeus.tamu.edu (Omezee-SaMe Still)
Newsgroups: comp.ai.neural-nets
Subject: Need info on Neural Net software for pattern classification.
Keywords: Neural networks,pattern classification,software.
Date: 10 Oct 92 17:18:00 GMT
Organization: Texas A&M University, Academic Computing Services
News-Software: VAX/VMS VNEWS 1.41



Hi!
I am interested in Neural Net software packages, that can be used
for pattern classification applications. I want to use them for
pattern classification in manufacturing. If anyone knows of some
codes or packages that I can either buy or access (ftp) please let 
me know at this email:
osm8502@summa.tamu.edu

Omez Samoon Mesina.
Texas A & M University,
Dept. of Mech.Engg.  
Newsgroups: comp.ai.neural-nets
From: usatoday@daimi.aau.dk (Peter Dalsgaard)
Subject: How to compute the inverse Hessian matrix FAST
Organization: DAIMI: Computer Science Department, Aarhus University, Denmark
Date: Tue, 13 Oct 92 20:15:49 GMT

Well... The subject line pretty much tells the story

Any answers will be greatly appreciated

Peter

-- 
   *** Peter Dalsgaard                        ////    ***  
   *** usatoday@daimi.aau.dk                 ////     *** 
   *** Computer Science Department      \\\\////      ***
   *** University of Aarhus, Denmark     \\//// Amiga ***
From: djasson@afterlife.ncsc.mil (Drew Asson)
Newsgroups: comp.ai.neural-nets
Subject: Eberhart paper on Explanation facilities for NNs
Date: 8 Oct 92 16:59:04 GMT
Distribution: usa
Organization: The Great Beyond

I got a reply and have a complete citation now.

Eberhart & Dobbins, "Designing Neural Network Explanation Facilities using
	Genetic Algorithms," Proceedings of the International Joint Conference
	on Neural Networks, Singapore, November 1991.

Mr. Eberhart said that a revised version of this paper will be published with
a series of papers from the June 1992 Combinations of Genetic Algorithms and  
Neural Networks Syposium held in Baltimore.  This will be forthcoming from
the IEEE Computer Society Press.

Sorry for the delays in getting this reference.

-- Drew
djasson@afterlife.ncsc.mil
From: sef@sef-pmax.slisp.cs.cmu.edu
Newsgroups: comp.ai.neural-nets
Subject: Call for Workshop Presenters
Date: 13 Oct 92 05:39:30 GMT
Organization: School of Computer Science, Carnegie Mellon


As you may have seen in the NIPS*92 workshop program, I will be running a
session entitled "Reading the Entrails: Understanding What's Going On
Inside a Neural Net".  This will take place on December 4 in Vail,
Colorado.

We will have a total of four hours for this workshop, which means we can
accommodate 6-8 presentations of about 20 minutes each (plus ample time for
discussion).  Several presentation slots are still open.

I would like to hear from any of you who would like to present a technique
that you have found useful for understanding what's going on inside a
network, either during or after training.  You don't necessarily have to be
the person who *invented* the technique, though you should have some real
hands-on experience.  As a presenter, you should describe how a specific
technique works, show (perhaps with diagrams or a videotape) how the
technique was applied to some specific problem, and describe what useful
insights resulted from this application.

Specific issues we would like to hear about include the following:

* How do you extract a set of rules (symbolic or fuzzy) from a trained
  neural network?  Under what conditions is this possible?

* How do you explain an individual network output or action in terms of the
  networks inputs and structure?  Which inputs are most responsible for
  this output?

* What are the best ways to visualize weights, unit states, and their
  trajectories over time?  How can we visualize the joint behavior of a
  large number of units?

* What can we learn from receptive-field diagrams for the hidden units?

* How can we understand the behavior of recurrent and time-domain
  networks?  (Extracting equivalent finite-state machines, etc.)

* Learning pathologies and what they look like.

If you would like to present something along these lines, please contact me
by E-mail (sef@cs.cmu.edu) and let me know what you would like to describe.

By the way, none of the NIPS workshops are limited to presenters only.
People who want to show up and listen are welcome, as long as there is
room.  It is suggested that you register pretty soon, however.
All inquiries for registration information should go to

	NIPS*92 Registration
	SIEMENS Research Center
	755 College Rd. East
	Princeton, NJ 08550

	phone 609-734-3383
	email kic@learning.siemens.com

See you in Vail!

-- Scott

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

Internet: sef+@cs.cmu.edu
From: sef@sef-pmax.slisp.cs.cmu.edu
Newsgroups: comp.ai.neural-nets
Subject: Re: Q: FSA & Neural-net, relationship?
Date: 7 Oct 92 13:35:16 GMT
Article-I.D.: cs.Bvr72w.pu.1
Organization: School of Computer Science, Carnegie Mellon


    From: yewhan@tartarus.uwa.edu.au (Yew Han Hee)
    
    I am currently doing a project related with Finite State Automaton, and
    I should find a relationship between FSA and Neural-net, could someone
    please give me some idea how they are related with each other?

It depends on what kind of neural network you have in mind.  A standard
backprop net is essentially combinational logic -- no internal state except
for the weights, which are constant after training.  Other models introduce
units with state or delay/feedback loops that allow the net to assume
various internal states.  For example, my own Recurrent Cascade-Correlation
networks (paper in NIPS 3) add hidden units that act as individual
flip-flops in the course of training.

Most such nets are not *really* finite-state, since the internal units can
take on a continuous range of values, but it's straightforward to partition
the internal states into a few equivalence classes that explain the net's
behavior -- in effect, to extract an equivalent FSM description from a
continuous-state machine.  Lee Giles and his colleagues have done some
interesting work in this area -- see their paper in NIPS 4.

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

Internet: sef+@cs.cmu.edu
From: Benedict.M.Wightman@cm.cf.ac.uk (B M Wightman)
Newsgroups: comp.ai.neural-nets
Subject: Re: Do neurons fatigue?
Date: 13 Oct 92 10:45:22 GMT
Distribution: comp
Organization: University of Wales College of Cardiff

>hucka@engin.umich.edu (Michael Hucka) writes:
>: Hello,
>: 
>: I would like to try to find out whether there is evidence for fatigue in
>: neurons.  Specifically, I'm interested in finding out whether there is
>: evidence for fatigue effects in non-receptor neurons in vertebrates, in
>: particular in cortical cells.  By fatigue I mean a decrease in responsiveness
>: of a particular cell as a result of continued stimulation, and not a process
>: of habituation.

What about the gills of Aphyslia, the seaslug? (What about them I hear you cry)
I'm not sure what the non-receptor bit means, but apparently the neurons related
to retracting the gills of the aforementioned aquatic (though I'm not sure about
vertebrate) creature do strange and unusual things when subjected to repeated
stimulation. Go and ask any respectable biologist (though how someone who 
tickles slugs can be seen as respectable, I don't know)
--
- Benedict.M.Wightman@cm.cf.ac.uk / COMMA,Senghenydd Rd.,Cardiff,Wales -
-------- PhD Student in Meddling In Things I Do Not Understand ---------
Recipe for disaster:
Computer system, important task to be solved, deadline, me. Mix well, wait.
From: frank@proteus.qc.ca (Frank Salvatori)
Newsgroups: comp.ai.neural-nets
Subject: Re: Stock Market Database
Keywords: neural net, stock market
Date: 9 Oct 92 16:45:59 GMT
Reply-To: frank@proteus.qc.ca (Frank Salvatori)
Organization: The Proteus Group, Montreal Canada

bryant@neural.Kodak.COM (Steve Bryant) writes:

> Does a stock market performance data base exist for any of the
> popular indexes? I would like to test a network against stock
> market prediction.

What specifically are you looking for. There are many indexes, and
many databases. What kind of tests do you want to do ?

---
Frank Salvatori 
The Proteus Group               Internet: frank@proteus.qc.ca
Montreal, Quebec, Canada        uucp: uunet!proteus!frank
Newsgroups: comp.ai.neural-nets