HDTenEightyP/NetNews huggingface dataset:
From: arms@cs.UAlberta.CA (Bill Armstrong)
Subject: Re: need for unique test sets
Organization: University of Alberta, Edmonton, Canada
Date: Tue, 21 Jul 1992 21:42:52 GMT

marcus@sun-of-smokey.NoSubdomain.NoDomain (Jeff Marcus) writes:
... lots of deletions.

>However, the more test data I have, the more sure I am that the
>result on the test data is a good predictor of the classifier's performance on
>new data. This can be quantified with a confidence interval on my
>classification
>error. This scheme is only inadequate in the sense that I can never
>achieve
>a zero confidence bound. But I don't see why that is important.

>Of course, all this assumes that I have a good way of
>selecting a representative sample in my test set, a non-trivial problem
>in its own right.

> you can frame performance
>estimation as
>a statistical estimation problem and the same arguments I just made
>would apply: namely,
>that as you get more data, you get more confident of your result, but
>you can never
>be sure that your result is exactly right.

I agree with you, that we have a statistical estimation problem, but
my point is that people shouldn't have to be happy with just a statistical
estimate of the probability of error.  Sometimes, as in cases where
safety is critical, you would like to have a system that can be proven to
do the right thing in all cases, even though the number of cases is
astronomical, far beyond the possibilities for testing.

Take software development, for example.  Would you accept testing
alone as the basis for declaring a piece of software safe?  Say it is
to be used in a nuclear reactor control system.  What about the 10000000-th
path through the code that you didn't test for?  What I want to propose is
that we in the NN field have to develop NNs that are at least as reliable as
commercial software, and preferably even more reliable.


--
***************************************************
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
From: henrik@mpci.llnl.gov (Henrik Klagges)
Newsgroups: comp.ai.neural-nets
Subject: Re: ftp sites for tech reports
Keywords: cascor
Date: 27 Jul 92 15:51:48 GMT



Try 128.146.8.52 , dire. /pub/neuroprose. Get the INDEX and grep
for cascor/cascade etc.

--

Cheers, Henrik
MPCI at LLNL
IBM Research
Newsgroups: comp.ai.neural-nets
From: arms@cs.UAlberta.CA (Bill Armstrong)
Subject: Re: Neural Nets and Brains
Organization: University of Alberta, Edmonton, Canada
Date: Tue, 28 Jul 1992 15:30:44 GMT

garry@mdavcr.mda.ca (Gary Holmen) writes:

>After reading this message I decided to go home and run the ALN software
>on my IBM PC. It is only a 25 Hz 386 with 2 Meg of memory and the 
>package seemed to work fine. I trained 3 trees on the times tables and
>it finished in about 45 mins. The answer I recieved was 1*7 which is about
>the same accuracy I've seen from the BP algorithms I've used.

He continues, referring to Mike Black's test:
 
>My guess the problem with your answer had to do with the 
>random_walk portion of the algorithm rather than the ALN's themselves.
>I believe that this has been noted for modification in future releases.

The fact is that ALNs do not interpolate.  How could they if they
don't do arithmetic? ALNs generalize by being insensitive to
perturbations of the inputs.  The anwer you obtained is just what I
would expect: the anwer to 1*6 which was missing from the training
set, was generalized from 1*7 which was in it.  Mike's problem where
he obtained ~35 (was it?) is possibly due to a bad coding of the
output variable (not enough bits)

By the way, 3 trees only won't do it.  I suppose you mean 3 trees per bit
of the output code?

In the next version of our software, we are scrapping the random walk
technique.  It works, but it doesn't fit in to a safe design
methodology.  Interpolation will be handled by smoothing the "blocky"
functions that ALNs produce.  (There is a close relation to BP here.
We will use something like the derivative of the usual sigmoid as a
kernel, but chopped to a bounded support and made infinitely-often
differentiable.  The use of a finite support makes the result a lot
faster to compute.)

> ... Look at them objectively and learn their advantages 
>and disadvantages so that we can use neural networks to their full potential.

Thanks. One can't ask more.
--
***************************************************
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
From: jim@netlink.cts.com (Jim Bowery)
Newsgroups: comp.ai.neural-nets
Subject: Body transplants
Date: 25 Jul 92 14:38:02 GMT
Organization: NetLink Online Communications, San Diego CA

I just saw a brief part of a show on a researcher who has been
transplanting the bodies of primates and apparently is now in a
position to attempt to do so with humans -- or so he believes.
His claim is that it is actually more difficult to transplant
limbs than it is an entire body.  Why this would be so or whether
this guy is off in left field I don't know.  But the show was on
public broadcasting and delved into the ethical issues quite
extensively.  The researcher is a devout Catholic.

--                    
INTERNET:  jim@netlink.cts.com (Jim Bowery)
UUCP:   ...!ryptyde!netlink!jim
NetLink Online Communications * Public Access in San Diego, CA (619) 453-1115
Newsgroups: comp.ai.neural-nets
From: rka@fct.unl.pt (Rafaela Kufahl Azinhal)
Subject: Re: comp.speech?
Organization: Universidade Nova de Lisboa -- Lisbon, Portugal
	<1992Jul27.014617.2969@newshost.anu.edu.au>
Date: Wed, 29 Jul 1992 18:52:55 GMT

As you mentioned, this group would combine several AI techniques as
neural-nets, decision trees, genetic algorithms. To mantain linearity,
I would call it comp.ai.speech.
--
+---------------------------------------------------------------------+
| Rafaela Kufahl Valente Azinhal    | BITNET/Internet: rka@fct.unl.pt |
| Universidade Nova de Lisboa       |          UUCP: rka@unl.uucp     |
| FCT - Dpto. de Informatica        | Fax:   (+351) (1) 295-5641      |
| Quinta da Torre                   | Telex: 14 542 FCTUNL P          |
| 2825 Monte Caparica, PORTUGAL     | Phone: (+351) (1) 295-4464      |
+---------------------------------------------------------------------+
From: hutching%elan.utah.edu@cs.utah.edu (Brad Hutchings)
Newsgroups: comp.ai.neural-nets
Subject: Carver Meads VLSI CAD tools
Date: 21 Jul 92 18:06:41 GMT
Organization: University of Utah CS Dept

Hi,

Can some kind soul tell me where I can download Carver Mead's Analog
VLSI design tools?

Thanks,

Brad

hutching@cs.utah.edu
From: sef@sef-pmax.slisp.cs.cmu.edu
Newsgroups: comp.ai.neural-nets
Subject: Re: any complicated units/connections used ?
Date: 27 Jul 92 14:22:02 GMT
Article-I.D.: cs.1992Jul27.142202.290451
Organization: School of Computer Science, Carnegie Mellon


    From: prechelt@i41s14.ira.uka.de (Lutz Prechelt)
    
    in almost all neural network models used today, the connections are 
    restricted to multipying their input with a weight and the units 
    are restricted to applying a mapping function (e.g. threshold or 
    squashing function) to their cumulated inputs.
    
    I would like to know about all *more complicated* forms of units or
    connections anybody is using or planning to use in a neural network.
    I am aware of sigma-pi units, activation decay, and time delay 
    connections but please send me info about how you use these, anyway.
    
The key question to ask about nets with more complex elements is whether
the added complexity buys you anything important.  For example, could each
of the more complex units be simulated by a little cluster of backprop
units.

Even if there's some formal equivalence, the more complex units may be
useful if they introduce some constraint or structure into the net that was
not there in backprop.  This may lead to faster training or better
generalization.  For example, sigma-pi units are not fundamentally more
powerful than plain old sigma units with an extra layer or two, but they
seem to be well-matched to problems that want to activate different rule
sets (or overlays on a fixed rule set) at different times.  For example,
such units have been used to good advantage in speech understanding to
blend together several different speaker models.  (See paper by Hampshire
and Waibel in NIPS-2.)

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

Internet: sef+@cs.cmu.edu
From: bill@nsma.arizona.edu (Bill Skaggs)
Newsgroups: comp.ai.neural-nets
Subject: Re: Neural Nets and Brains
Date: 24 Jul 92 05:45:39 GMT
	<arms.711907358@spedden> <BILL.92Jul23135614@ca3.nsma.arizona.edu>
	<arms.711935064@spedden>
Organization: ARL Division of Neural Systems, Memory and Aging, University of
	Arizona

arms@cs.UAlberta.CA (Bill Armstrong) writes:

   >The theory of multilayer perceptrons as used in BP is horrifying.
   >You need Kolmogorov's theorem just to show you can do everything
   >you want to approximate continuous functions, but nobody can apply
   >it in practice.  In contrast, every logic designer who has heard
   >of CNF and DNF finds it *obvious* that a adaptive logic net can
   >synthesize any boolean function.  So BP nets lose in a BIG way on
   >the theory side, sorry. 

Well, McCulloch and Pitts proved a long time ago that their networks
could emulate any Turing machine; it's hard to get any any stronger
than that.

You provoked this argument with a claim that ALNs are the only
reasonable approach to understanding the brain.  I don't dispute that
ALNs are very powerful and very interesting, but that claim is hubris
of the worst kind.  The argument from theoretical superiority is
simply irrelevant.  Mother Nature is an engineer, not a theoretician,
and the brain is a collection of kludges -- some of them more elegant
than we have any right to expect -- put together by trial and error.
I'm not a big fan of backprop, but it does have the advantage of
simplicity to counterbalance the disadvantage of inefficiency, and
there is no way of knowing *a priori* which of these Mother Nature has
considered more important.

	-- Bill
From: spedk@thor.cf.ac.uk (mr_d_karaboga_92)
Newsgroups: comp.ai.neural-nets
Subject: paper on genetic algorithms and fuzzy logic controllers
Date: 21 Jul 92 19:30:16 GMT
Organization: University of Wales College of Cardiff, Cardiff, Wales, UK
X-Mailer: Cardiff Computing Maths PP Mail Open News Gateway


         Optimum Design of Fuzzy Logic Controllers
                Using Genetic Algorithms

      (J of Systems Engineering, vol.1, no.2, 114-118)

                D.T. Pham and D. Karaboga

                       Abstract

      This paper describes the use of genetic algorithms 
      (GAs) to design fuzzy logic controllers (FLCs). Two
      GAs are considered, a basic GA and a modified GA. 
      The modified GA employs 'cross breeding' between 
      different gene populations to produce a fitter
      population. Simulation results are presented which
      demonstrate the ability of the modified GA to design
      better FLCs than those obtained with the basic GA.

      Correspondence and offprints requests to:
      phamdt@taff.cardiff.ac.uk
      or
      Karaboga@taff.cardiff.ac.uk

      Intelligent Systems Research Laboratory, University
      of Wales College of Cardiff, Cardiff, CF1 3YH, UK.
From: omlinc@williams.cs.rpi.edu (C Omlin)
Newsgroups: comp.ai.neural-nets
Subject: time series prediction
Date: 30 Jul 92 20:12:29 GMT
Organization: Rensselaer Polytechnic Institute, Troy NY

Hi !

I am trying to get an overview on application of neural networks
(both feedforward and recurrent) to time-series prediction.

Please send references on the topic to the address below. I'll
summarize the replies in this newsgroup.

Thanks.

Christian



----------------------------------------------------------------------------
Christian W. Omlin		   Troy, NY 12180 USA			
Computer Science Department        Phone: (518) 276-2930 Fax: (518) 276-4033   
Amos Eaton 119			   E-mail: omlinc@cs.rpi.edu             
Rensselaer Polytechnic Institute           
----------------------------------------------------------------------------
From: black@seismo.CSS.GOV (Mike Black)
Newsgroups: comp.ai.neural-nets
Subject: Re: Neural Nets and Brains
Date: 25 Jul 92 15:25:41 GMT
Organization: Center for Seismic Studies, Arlington, VA

In article <arms.711935064@spedden> arms@cs.UAlberta.CA (Bill Armstrong) writes:
>
>The theory of multilayer perceptrons as used in BP is horrifying.  You
>need Kolmogorov's theorem just to show you can do everything you want
>to approximate continuous functions, but nobody can apply it in
>practice.  In contrast, every logic designer who has heard of CNF and
>DNF finds it *obvious* that a adaptive logic net can synthesize any
>boolean function.  So BP nets lose in a BIG way on the theory side, sorry.
>
I couldn't care less about theory...only results...

>As for learning, BP is just gradient descent, and it leads to networks
>that are *extremely* inefficient -- like trying to write C programs
>with no conditional statements like "if" or "for" or "while".  For
>adaptive algorithms in logic networks that are far superior to
>backprop, you can take a look at the atree release 2.6 adaptive logic
>network simulator.
>
I will argue the superiority shortly...

>Sorry, but after you have looked at ALN software, you may no longer
>feel non-logical nets have any real advantages at all.
>

I took the atree software and (for times sake) reduced the multiplication
problem to the 1 and 2 times tables.  I removed 1*6 from the table and
let atree crank.  When I tested 1*6 it gave me an answer of ~35.  I do
NOT call this superior as the backprop net I trained gave me an answer
that was at least BETWEEN 1*5 and 1*7.   The other problem I ran into
was running out of memory (16 meg + 64meg swap space) on a problem that
I had previously solved with backprop.  

My conclusions:

1.  backprop is able to generalize to a linear solution whereas atree
cannot (in at least one provable case).

2.  atree hits memory constraints before backprop does.

3.  I have no doubt that atree does well in certain applications, but
it NOT superior to backprop in ALL cases.

-- 
-------------------------------------------------------------------------------
: usenet: black@beno.CSS.GOV   :  land line: 407-494-5853  : I want a computer:
: real home: Melbourne, FL     :  home line: 407-242-8619  : that does it all!:
-------------------------------------------------------------------------------
From: Daniel.K.R.McCormack@cm.cf.ac.uk (Daniel K R McCormack)
Newsgroups: comp.ai.neural-nets
Subject: Yet more refs wanted !!
Date: 30 Jul 92 11:54:15 GMT
Organization: University of Wales College of Cardiff, Cardiff, Wales, UK.

Hi,

	I'm looking for some refs on the following subjects :-

1)	Parallel implementations of Neural Networks. 
 	Particulary for the Ncube machine but any refs would
	be appreciated.

2)	NN ES hybrids. References to their use and what they
	are capable of doing etc..

Many Thanks in advance

I will summarise if requested

	Danny
From: pakst5@gl.pitt.edu (Paul A Kwan)
Newsgroups: comp.ai.neural-nets
Subject: Re: Carver Meads VLSI CAD tools
Date: 21 Jul 92 20:29:38 GMT


     Ftp    : hobiecat.cs.caltech.edu
              login: anonymous
              passwd: username
              cd ~ftp/pub/chipmunk
paul
From: cisneros@cs.ucf.edu (Jaime Cisneros)
Newsgroups: comp.ai.neural-nets
Subject: Stock market analysis
Date: 23 Jul 92 17:51:38 GMT
Organization: University of Central Florida, Orlando

Hello everyone,
             I would like to know if there is anyone
out there who has some information on stock market
analysis.  Are there any programs?  If so, are
any of them ftpable?  Are there any good references?
Is there any reliable work on this area?
   If someone can help, I will appreciate it very
much.

  Jaime Cisneros

P.S. Please send any information to cisneros@cs.ucf.edu.
Newsgroups: comp.ai.neural-nets
From: akerberg@tree.egr.uh.edu (Peeter M. Akerberg)
Subject: Question: Can you compare ANNs direcly? How?
Keywords: literature request
Reply-To: akerberg@tree.egr.uh.edu (Peeter M. Akerberg)
Organization: University of Houston
Date: Mon, 27 Jul 1992 20:23:59 GMT

My question is concerned with comparing ANNs with identical topology but  
with different sets of connection weights. I would like to compare these  
nets directly, based on the weights only.
 Based on what I've read I got the impression that training a ANN on the  
same data set to perform the same task may converge to a different set of  
weights if e.g. the data is presented in a different order. I'm hoping to  
be able to compare disjoint subset from a reasonably homogenous set of  
independent samples. These subsets would certainly result in different,  
but hopefully similar in some sense, nets. It is such a measure of  
similarity I would like to know if anyone has found.
 
 Please e-mail to akerberg@tree.egr.uh.edu if you have heard or seen  
anything about this issue. I haven't found anything in the literature so I  
would be very grateful for any hints to references or for any thoughts  
about the possibility for such comparisons.
I'll summarize if anyone is interested.

Peeter Akerberg
Graduate student Electrical Engineering at University of Houston
From: santulli@europa.informatik.uni-dortmund.de (Marco Santulli)
Newsgroups: comp.ai.neural-nets
Subject: activation dynamics in neurons
Date: 24 Jul 1992 17:13:38 GMT
Organization: University of Dortmund, Germany
Distribution: world
NNTP-Posting-Host: europa

I'm looking for a detailed description of the activation
dynamics of the real neuron.

Are they equal for every neuron, or is there a differenciation
in the various functions of neurons with different acitvities??

Please post answers or mail them to:

-----------------------------------------------------------
Marco santulli                                   carpe diem
santulli@jupiter.informatik.uni-dortmund.de

Dept. of Computer Science I  University of Dortmund Germany
-----------------------------------------------------------
From: lordSnooty@cup.portal.com (Andrew - Palfreyman)
Newsgroups: comp.ai.neural-nets
Subject: Re: Bayes Classifier & Cluster Analysis software required
Date: Fri, 24 Jul 92 19:58:08 PDT
Organization: The Portal System (TM)

Irwin Gibson:

: Bayesian Classifier & Cluster Analysis
: =======================================
: 
: If anyone has or knows of the following types of software and there
: availability: (ftp or email)
: 
: Bayesian Classifier & Cluster Analysis (DOS or UNIX)
: 
: This software is urgently required for use in Neural Network Research.

A NASA Tech Brief recently oredered and received by me contains some
fascinating new twists on Bayesian Cluster Analysis. There is a program
available, called AutoClass II, for automatically deducing classes from
a database (_a propos_ of which, my posting on this theme was met with 
a resounding silence from this froup :-)).

Try cheeseman@pluto.arc.nasa.gov for more details (Peter Cheeseman).
 --------------------------------------------------------------------------
| lord snooty @the giant | how DARE they cancel Dennis Miller?             |
| poisoned electric head |                               andrew palfreyman |
 --------------------------------------------------------------------------
Newsgroups: comp.ai.neural-nets
From: hoekstra@cs.utwente.nl (Aarnoud Hoekstra)
Subject: SOFM, SOM et. al.
Keywords: SOFM, SOM
Organization: University of Twente, Dept. of Computer Science
Date: Wed, 29 Jul 1992 09:28:43 GMT

Dear Netters,

Since my last call for references I received a few reactions (thanks !!), but
I'm wondering how many people are working with SOFM or SOM etc.

I hardly ever read anything on this news group about these maps ( :-( ). Is it
because the SOM workers are working hard and have no time to read this group ?
I would be very happy if we could discuss these maps on this newsgroup.

Greetings from Holland,

  /\/\/\
 |      |
 |      |  You know, the land of the tulips and others of course
  \    /
    ||
    ||

Aarnoud Hoekstra
Computer Science Department
University of Twente
hoekstra@cs.utwente.nl
From: davisd@milton.u.washington.edu (Daniel Davis)
Newsgroups: comp.ai.neural-nets
Subject: Re: Neural Nets and Brains
Date: 24 Jul 92 23:58:28 GMT
Organization: University of Washington, Seattle

In article <arms.711986585@spedden> arms@cs.UAlberta.CA (Bill Armstrong) writes:
>
>I don't fel quite comfortable with the way you phrased that.  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? 

There are many levels upon which one can model neuron function. When
you choose to view it as 0-1 signals, you are stressing the action
potentials propensity to spike to a maximum level. A neuron need not
produce a spike, and instead the action potential can be viewed as a
continuous function. If one wants finer detail, one can note that the
action potentials are caused by transmission of neurotransmitters, and
so one can talk about discrete systems again. (That is about as fine
detail as I would care to go. No doubt one can talk quantum mechanical
effects, if one is so inclined.)

I personally do not believe that viewing neuron function in a binary
fashion will be fruitful until one models the sequencing and rate of
firing of action potentials. As for the non-applicability of
continuous functions to neuron functioning, I would suggest that
continuous valued artificial neurons can be viewed as modelling the
rate at which different neurons fire action potentials. (The rate of
fire is often what conveys information, not simply whether or not a
neuron has fired.)

Finally, it should be remembered that most of us are not in fact
trying to model the brain, but are instead using insights from brain
function (mainly the notion of distributed processing),
_along_with_insights_gained_from_other_fields_, as design guides for
information processing systems. Whether or not one can make analogies
between one's chosen structure and a brain is often irrelevant.

Buy Buy -- Dan Davis, Univ. of Wash., Dept. of EE,
davisd@u.washington.edu
Newsgroups: comp.ai.neural-nets