HDTenEightyP/NetNews huggingface dataset:
From: lene@swix.nvg.unit.no (Lene Jensen)
Subject: Counterpropagation
Organization: Nettverksgruppa - UNIT
Date: Thu, 21 Jan 93 17:59:35 GMT

Hello!

I was wondering if anyone could help me find litterature about
counterpropagation.  I am writing about neural nets in use in
communications, and I need to read anything I can find on that
subject.  In advance, thanks.

Lene Jensen

-- 
          Lene Jensen          0+>  If a man is considered guilty 4 what
     University of Trondheim   0+>  goes on in his mind, then give me the
   College of Art and Science  0+>  electric chair 4 all my future crimes,
      lene@swix.nvg.unit.no    0+>  'Cause I'm guilty...   /Prince
Newsgroups: comp.ai.neural-nets,bionet.neuroscience
From: mitchm@casbah.acns.nwu.edu (Mitchell Maltenfort)
Subject: Re: SNNS simulator
Keywords: Neural nets, simulator
Organization: Northwestern University, Evanston Illinois.
Date: Thu, 21 Jan 1993 22:27:17 GMT

In article <1993Jan21.160658.18051@ucl.ac.uk> Shiv Kaul writes:

>I have obtained the SNNS (Stuttgart Neural Net Simulator) from 
>Stuttgart University via anon ftp and compiled it on an IBM RS6000
>workstation.  The last bit of installation fails with the complaint that
>the 'xgui' file (the one that starts the system!) cannot be found.
>
>


	Might I ask someone to email me with instructions for aquiring and
installing the SNNS on an RS6000?  Thanks!


Mitchell Maltenfort	Northwestern Unversity	Chicago, Illinois

mgm@nwu.edu		|:* Studying simulations or simulating studies *:|

Newsgroups: comp.ai.neural-nets,comp.speech
From: pclink@cheops.qld.tne.oz.au (Rick)
Subject: Q: Teaching a neural net to speak with an accent.
Organization: Telecom Australia, TNE Computer Support Services
Date: Sun, 24 Jan 1993 23:34:59 GMT

I want my PC controlled answering machine to answer the phone with
the voice of Inigo Montoya (Princess Bride, wanders around saying
"'Ello.  My name is Inigo Montoya.  You killed my father.  Prepare to
die").  The problem is that during the entire movie, Inigo does not once
say "If you do not leave a message at the tone, prepare to die".  Last week
there was a show on the vidiot box that showed a neural net learning to
speak a phrase by feeding back the differnces between its attempt and the
utterance by a human.  My question is, is it possible to teach a net to the
point where it can correctly pronounce words it has not seen/heard?  A
related question arises because of the fact that the printed word has a low
information content compared to speech.  Is there a standard way of
encoding speech in text such that things like pitch, volume and emotional
content can be transmitted?  Is the above project feasible, or will the
computational requirements mean waiting until we have a Cray-in-a-wristwatch?
Note that I don't require real-time text to speech - I don't mind if the net
requires several 100M and a week to generate the speech, as long as I can
collect the output an play it back in real-time.

Thanks,
Rick.
Xref: sparky comp.ai.neural-nets:4971 bionet.neuroscience:692
From: Shiv Kaul
Newsgroups: comp.ai.neural-nets,bionet.neuroscience
Subject: Re: SNNS simulator
Keywords: Neural nets, simulator
Date: 25 Jan 93 14:30:46 GMT
Organization: Bloomsbury Computing Consortium
Organisation: University College London

osm8502@rigel.tamu.edu (Omezee-SaMe Still) writes:

>Please let me know where and how I can obtain the SNNS. 

The SNNS simulator can be obtained via anonymous ftp from host

ifi.informatik.uni-stuttgart.de (129.69.211.1)

in the subdirectory:

/pub/SNNS

as file:

SNNSv2.1.tar.Z

Best regards,

Shiv Kaul



--
-------------------------------------------------------------------------------
Department of Physiology       : Tel: (071) 387 7050 x3248 : 
University College London      : Fax: (071) 383 7005       :  
Gower Street, London WC1E 6BT  : Email: s.kaul@ucl.ac.uk   :    
Newsgroups: comp.ai.neural-nets
From: kadie@cs.uiuc.edu (Carl M. Kadie)
Subject: Re: Data for Travelling Salesman Prob Wanted...
Organization: University of Illinois, Dept. of Comp. Sci., Urbana, IL
Date: Fri, 22 Jan 1993 20:05:29 GMT

uh311ae@sunmanager.LRZ-Muenchen.DE (Henrik Klagges) writes:

[...]
check out the paper by fritzke in the /pub/neuroprose archive at the aftp
server archive.cis.ohio-state.edu. He has a very slick 'cell-based' alg.
on TSP that has excellent scaling behaviour.
[...]

From looking at the paper, it looks as though the Fritzke's program
doesn't guarantee the quality of its solution. Based on the _Discover_
article, I think the Johnson's method can. Also, Johnson's program
seems to be 2 or 3 orders of magnitude faster and to produce better
solutions.  For comparison:

               Johnson                           Fritzke
# of cites:    1000000                           2392
CPU time:      3 cpu hours                       1.13 cpu hours
Quality:       within 2% of optimum              about 9% of optimum
Guaranteed
 error bounds: yes (I think)                     no

- Carl

Sources: _Discover_ magazine, Jan. 1992, p 91
         anonymous ftp
            archive.cis.ohio-state.edu/pub/neuroprose/fritzke.linear-tsp.ps.Z


-- 
Carl Kadie -- I do not represent any organization; this is just me.
 = kadie@cs.uiuc.edu =
From: ajay@cgl.ucsf.EDU (Ajay)
Newsgroups: comp.ai.neural-nets
Subject: Regression using Neural NEts
Date: 27 Jan 93 01:20:42 GMT
Reply-To: ajay@cgl.ucsf.edu
Organization: CGL

Hi,

    We know that multi-colinearity is a problem in normal linear
regression.

    Has there been any work in neural nets (applied to regression)
to check whether multi-colinearity would be a problem here too?

    Any references, ideas would be much appreciated.

Thanks.

	---ajay
Newsgroups: comp.ai.neural-nets
From: bxk@cs.bham.ac.uk (Brian Karlsen)
Subject: Request: NETtalk article
Keywords: spelling-to-sound, neural-net
Organization: University of Birmingham
Date: Thu, 28 Jan 1993 16:03:03 GMT

Hi there!

I wonder if any of you people have access to or know how to get hold of the 
original article/technical report about NETtalk by Sejnowski and Rosenberg:

 Sejnowski,T & Rosenberg,C (1986):"NETtalk: A Parallel Network That Learns to 
                                   read aloud",
                                  Johns Hopkins University Elec. Enc. and Comp.
                                  Sci. Technical Report, Jhu/EEC-86/01
Thanks in advance

 Brian Karlsen
 (bxk@cs.bham.ac.uk)
 MSc student in Cognitive Science
 at Birmingham University, UK
From: hillam@uivlsisi.csl.uiuc.edu (Anthony Martin Hill)
Newsgroups: comp.ai.neural-nets
Subject: Re: Olfactory Cortex Simulation
Date: 25 Jan 93 04:22:58 GMT
Organization: Center for Reliable and High-Performance Computing, University of Illinois at Urbana-Champaign
NNTP-Posting-Host: uivlsisi.csl.uiuc.edu

As you know Granger and Lynch published two or three papers on their
studies, but none of these is really thorough enough to understand
what their model really does.  We did some work on simulating this
particular system at Oklahoma State (actually it's still going on
in conjunction with NOSC in San Deigo).  We found that the best 
reference for this system comes from a graduate student who also
worked on this project with Granger and Lynch.  I believe his name
was Ambros (but don't hold me to that.)  His name appears with
Granger and Lynch in a number of articles dealing with this
system so it's best to get it there.  Anyway, his PhD dissertation
was on this particular system.

As I said, while I was Oklahoma State University, I worked with 
Dr. Chris Hutchens to develop a software simulation of this system
to later be implemented in hardware.  As far as I know they are
still working on this.  You can probably get in contact with Dr.
Chris Hutchens c/o Department of Electrical and Computer Engineering,
Oklahoma State University, Stillwater, OK 74078, but like I said
it was in conjunction with the government so I don't know if he'll
be able to divulge much - other than references.
From: choy@dvinci.USask.Ca (Henry Choy)
Newsgroups: comp.ai.neural-nets
Subject: fault tolerance?
Date: 28 Jan 1993 02:04:13 GMT
Organization: University of Saskatchewan
NNTP-Posting-Host: dvinci.usask.ca
X-Newsreader: TIN [version 1.1 PL6]


How fault tolerant are neural nets? Inquiring minds want to read some
publications.

--

Henry Choy
choy@cs.usask.ca

What rolls down stairs alone or in pairs            This has been brought to 
Rolls over your neighbor's dog?                     you by the numbers 4
What's great for a snack and fits on your back?     and 9 and the letter P.
It's Log, Log, Log!  -- "The Log Song", from            -- Big Bird
                         Ren & Stimpy

Math is tough!       -- Barbie
Newsgroups: comp.ai.neural-nets
From: cantwell@vuse.vanderbilt.edu (Jeff Cantwell)
Subject: Wanted: Heuristics on Setting Neural Net Parameters
Organization: Vanderbilt University School of Engineering, Nashville, TN, USA
Date: Thu, 28 Jan 1993 03:18:33 GMT


I am looking for some heuristics for practical use of neural networks.
For instance, how should momentum, slope, magnitude, step size, and
other parameters be varied if the network fails to converge? How do
you decide how many hidden units are needed? Are there generally
applicable rules, or is everything "black magic"?  Any pointers to
articles or papers would be greatly appreciated. 

-Jeff



-- 
Jeff Cantwell, Vanderbilt U. CIS Box 1804, Sta. B Nashville, TN 37235 
  Internet: cantwell@vuse.vanderbilt.edu Bitnet: CANTWEJR@VUCTRVAX
	Phone: (615) 322-0110  Fax: (615) 343-6449
       2 Cor. 12:9 "Power is perfected in weakness."
From: mconeil@afterlife.ncsc.mil (Michael O'Neill)
Newsgroups: comp.ai.neural-nets
Subject: Question Regarding Linear Functions Fitted To Non-lin Systems
Date: 23 Jan 93 05:08:38 GMT
Organization: The Great Beyond


 
   I have a question that seems more difficult to answer succinctly the more I 
think about it.

   While it can be shown that a perceptron will eventually train to the correct
linear model of a linear system, provided resources requirements exist, does
this expectation be realized in the following case?

   Suppose a non-linear system for which a backprop network finds a solution
which is so nearly linear that it adequately maps input to output within the
limits of error.  This may be only one of a single class of approximators, or
one of a class of a group of approximators; I have not done that research.
Assuming, though, that the linear approximator is unique, does the same
"guarantee" which pertains to perceptron learning in linear systems apply to
nonlinear systems with good linear approximators?  That is, would a simple perceptron network find the same solution as the more complex backprop network?

						Thanks,
						MON
.
Newsgroups: comp.ai.neural-nets
From: piccolbo@ghost.dsi.unimi.it (antonio piccolboni)
Subject: Re: visualization
Organization: Computer Science Dep. - Milan University
Date: Thu, 28 Jan 1993 15:34:37 GMT

tgorb@rrc.chevron.com (Joe Gorberg) writes:

>In article 18443@den.mmc.com, asta@thermal4.den.mmc.com (Brent Cullimore) writes:
>>Is anyone out there interested in S/W tools for visualizing n-nets,
>>connection strengths, etc.?  Are there any standard tools already
>>for such visualization?
>>

>Although, I too am looking for a more powerful (functionally) visualization tool.
>Wouldn't it be nice if Virtual Reality was here now?
(I summarized)

I think Mathematica could turn out useful, because of its 4D visualization
 and data importing capabilities. Is there anyone who experienced this tool?

					Antonio Piccolboni






Newsgroups: comp.ai.neural-nets
From: cs_lkc@uxmail.ust.hk (Leung Ka Cheong)
Subject: Urge for a database on printed-character recognition
Organization: Hong Kong University of Science and Technology
Date: Mon, 25 Jan 1993 15:30:27 GMT


Hello, everybody :

      I'm now working on my project on character recognition which needs a hugh
database ( around 10000 data ) to work on.  The description on the requested
database is shown below :

         -  domain character : 0-9, A-Z
         -  each with a character pixel map ( 16 x 16 ) and the corresponding
            output in the format :
            ...
            [ character map ]
            [ character output, e.g. A ]
            <space>
            ...
         -  the map is in binary format preferably, or the pixel values ranged
            between 0 and 350 

        I hope that you can help me to provide this database to help me 
completing my project.

        I'm looking forward to your reply.

With regards,
cs_lkc

-- 
******************************************************************************
!   Full Name  :  Ka-Cheong LEUNG           |    MATH MATH MATH MATH MATH    !
!   School ..  :  Engineering               |   CCCCC  OOOOO  M   M  PPPPP   !
!   Programme  :  Computer Science ( UG )   |   C      O   O  MM MM  P   P   !
Newsgroups: comp.ai.neural-nets
From: bedros@ee.umn.edu (Saad John Bedros)
Subject: Looking for source code for Fuzzy ARTMAP (Grosberg, Carpenter)
Organization: Electrical Engineering, U of MN, Mpls.
Date: Fri, 22 Jan 1993 22:18:43 GMT

	I am looking for a source code for the Fuzzy ARTMAP by 
Carpenter and Grossberg mentioned in IEEE Communication magazine
Sept 1992

Thanks
Saad J Bedros

Newsgroups: comp.ai.neural-nets
From: gperkins@netcom.com (Glen C. Perkins)
Subject: Re: Need help on Neural-network learning
Organization: Netcom - Online Communication Services  (408 241-9760 guest) 
Date: Fri, 22 Jan 1993 18:12:11 GMT

tsa@cellar.org (The Silent Assassin) writes:

>I only recently got interested in neural networks.  I believe I understand
>how they work, but I have some questions:

>How many hidden layers do most neural networks have?  Does the typical NN
>have an input layer, a hidden layer, and an output layer, or several hidden
>layers?

There are a vast number of different architectures, but networks
having one or two hidden layers seem to be the norm.

>How do NN's learn?  Other than randomly changing weights, how do they
>"back-propagate" or whatever?

There are too many network architectures to answer this briefly. A
good explanation can be found in "Parallel Distributed Processing"
by Rummelhart and MacClellan (sp?) and several other introductory
books. You need the diagrams in the books to follow the explanations.

>I finally discovered, at the age of 21, what all of my friends had found out
>at 12 or 13-that your parents are just as fucked-up and wierd as everyone
>else.  Politically Correct:  Socially, Politcally, and Environmentally
>concious to the point of nausea.  tsa%cellar@tredysvr.tredydev.unisys.com

I found out at age 30 just how wonderful my parents were. Please
restrict your gratuitous social and political commentary to the
appropriate newsgroups.
-- 
+-----------------------+---------------------------------------------------+
|--- Glen C. Perkins ---|                                                   |
| <gperkins@netcom.com> |             "Don't forget, your mind              |
| Native Guide Software |              only *simulates* logic."             |
| Palo Alto, California |                                                   |
+-----------------------+---------------------------------------------------+

Newsgroups: comp.ai.neural-nets
From: vamplew@probitas.cs.utas.edu.au (Peter Vamplew)
Subject: Re: Recurrent networks
Organization: University of Tasmania, Australia.
Date: Thu, 28 Jan 1993 02:23:41 GMT

In <728134656.26714@minster.york.ac.uk> george@minster.york.ac.uk writes:

>I've been trying to get neural networks to learn sequences
>of type DIFFERENT->SAME->DIFFERENT states, e.g.

>A->B->C and X->B->Y

>is a simple example. However, with a recurrent network (feeding
>back the hidden layer), the output of a network for both pattern
>sequences can be given as: (fn O() is output layer, H() hidden)

>O(H(A)) and O(H(X)) both must equal B

>This seems to force the hidden representation for A and X to be
>identical. On the next step when B is given as input, the context
>fed back, i.e. the hidden representation H(A) or H(X), will
>not distinguish the network's state to output C or Y as appropriate.

>i.e. O(H(B, H(A))) = O(H(B, H(X)))

>How can this problem be overcome?

Why is it neccesary for H(A) = H(X)? This is true only if your output
function is strictly one-to-one. The network should be able to produce B as
an output for more than one possible set of hidden layer activations.

Peter

--
Peter Vamplew     Dept of Computer Science, Uni of Tasmania
"The second hand moves very quickly, the minute hand moves
 a bit more slowly, and the hour hand moves not much at all"
- results of a time and motion study by Reg the Slayer
From: glover@dvinci.USask.Ca (Ken Glover)
Newsgroups: comp.ai.neural-nets
Subject: Algorithms that adjust the size of the net
Date: 25 Jan 93 21:33:46 GMT
Article-I.D.: access.1k1mbqINNm0g
Organization: University of Saskatchewan
NNTP-Posting-Host: dvinci.usask.ca
X-Newsreader: TIN [version 1.1 PL6]


I am looking for information on any algorithms that adjust the size of
the network while it is training.  I know about the Cascade
Correlation one and described by Yoshio Hirose, Koichi Yamashita and
Shimpei Hijiya in a paper from Neural Networks, Volume 4, pp 61-66.
If anyone knows email addresses for any of those authors please also
send that information.

So, if you know of any algorithims and/or papers describing them that
do self adjust, please let me know.  Either e-mail or posting is ok.
If there is enough interest I will summarize.

Thanks
Ken Glover
Newsgroups: comp.ai.neural-nets
Subject: Data for Travelling Salesman Prob Wanted...
From: sarit@jupiter.cse.utoledo.edu (sarit sharma)
Date: 21 Jan 93 11:42:16 EST

hi,


i am looking for the data of the,
30 city Travelling Salesman Problem.
i have the algorithm, which i would like to test.
thanx.



the friendly ghost
sarit sharma
electrical engineering
university of toledo
toledo, oh
From: tgorb@rrc.chevron.com (Joe Gorberg)
Newsgroups: comp.ai.neural-nets
Subject: Re: visualization
Date: 26 Jan 93 19:27:58 GMT
Reply-To: tgorb@rrc.chevron.com
Organization: Chevron, Richmond, California

In article 18443@den.mmc.com, asta@thermal4.den.mmc.com (Brent Cullimore) writes:
>Is anyone out there interested in S/W tools for visualizing n-nets,
>connection strengths, etc.?  Are there any standard tools already
>for such visualization?
>
>If not, why not: other specialties using circuits, etc. rely
>on visualization for intuition, even when heavy number-crunching
>underlies it all.

I like to use JMP from SAS Institute Inc. for the Macintosh especially to
visualize 3D relationships.  This has been helpful for pre- and post- processing.
I also like to use the old mac/windows Excel spreadsheet for some basic analyses.

Although, I too am looking for a more powerful (functionally) visualization tool.
Wouldn't it be nice if Virtual Reality was here now?

Joe Gorberg
tgorb@rrc.chevron.com




Newsgroups: comp.ai.neural-nets
From: young@deci.cs.umn.edu (Mike Young)
Subject: Re: Jordan's recurrent neural network
Organization: University of Minnesota
Date: Sat, 23 Jan 1993 16:44:22 GMT

In <1993Jan22.205104.5685@oucsace.cs.ohiou.edu> mpmehta@bobcat.ent.ohiou.edu (Manish P. Mehta  ISE Grad) writes:

>What i am looking for is a software developed for Jordan's recurrent neural
>network. If any one has used or developed a software please mail that to me.
>Or if you know where I can get it please let me know...

The Tlearn software developed at UCSD's Center for Research in Language
is flexible enough to create recurrent networks of a type at least similar
to Jordan's.  I've used it to create Elman recurrent nets, but not Jordan's.
It seems to me that Jordan may have had a learning rate twist somewhere in
his work, and if so that particular aspect would not be implementable in 
Tlearn.  Give it a try. 

I obtained it via anonymous ftp from crl.ucsd.edu.  Let me go poke around to 
find the exact location...
   pub/neuralnets/tlearn.tar.Z 

Good luck. 

Mike Young
young@cs.umn.edu -or- young@turtle.psych.umn.edu
Newsgroups: comp.ai.neural-nets
From: kadie@herodotus.cs.uiuc.edu (Carl M. Kadie)
Subject: Re: Data for Travelling Salesman Prob Wanted...
Organization: University of Illinois, Dept. of Comp. Sci., Urbana, IL
Date: Thu, 21 Jan 1993 17:58:52 GMT

Here is some background information on state-of-the-art traveling
salesman problem solvers:

William Cook of Bellcore found an *optimum* TSP solution for a 3038
node circuit board with 1.5 cpu years of computer time.

David Johnson of ATT Bell Labs found a provably-within-2%-of-optimum
TSP solution for a millon cities in less than 3 cpu hours.

[Source: _Discover_ magazine, Jan. 1992, p 91]

(I don't believe either researcher is using artficial neural net.)

- Carl

--
Carl Kadie -- kadie@cs.uiuc.edu -- University of Illinois at Urbana-Champaign
From: mpmehta@bobcat.ent.ohiou.edu (Manish P. Mehta  ISE Grad)
Newsgroups: comp.ai.neural-nets
Subject: Software for Jordan's recurrent neural network
Date: 22 Jan 93 15:57:29 GMT
Organization: College of Engg. & Tech., Ohio University, Athens, Ohio
Originator: mpmehta@bobcat.ent.ohiou.edu

mpmehta@bobcat.ent.ohiou.edu

Thanks
Manish Mehta


-- 
*******************************************************************************
* 
*
* Home Address: Manish Mehta
From: laurens@metro (Laurens Leerink)
Subject: Re: Counterpropagation
Organization: Sydney University Computing Service, Sydney, NSW, Australia
Date: Thu, 21 Jan 1993 23:51:13 GMT

> I was wondering if anyone could help me find litterature about
> counterpropagation.  I am writing about neural nets in use in
> communications, and I need to read anything I can find on that
> subject.  In advance, thanks.

Chapter 6 of Freeman & Skapura's book (see below) is on counterpropagation
and also includes Pascal source code.  Full details of the book are 

@book{Freeman91,
       author = "Freeman, J.A. and D.M. Skapura",
        title = "Neural Networks : Algorithms, Applications and Programming Tec
hniques",
    publisher = "Addison-Wesley",
      address = "",
         year =  1991
  }

- Laurens

------------------------------------------------------------------------------
  Laurens Leerink                               | Phone: +61 2 692 2842
  School of Electrical Engineering              | Fax:   +61 2 692 3847
  The University of Sydney NSW 2006, Australia
------------------------------------------------------------------------------
Newsgroups: comp.ai
From: plassche@nici.kun.nl (Joke van de Plassche)
Newsgroups: comp.ai
Subject: knowledge acquisition tools wanted
Date: 21 Jan 93 15:07:54 GMT
Followup-To: comp.ai
Organization: University of Nijmegen, cognitive science

Could anybody tell me where I can find public domain knowledge 
acquisition tools for the Macintosh to use in a course on knowledge 
acquisition. As it is only for use in an introductionary course, I'm 
interested in a small, easy-to-use tool (e.g. reportory grid).

Thanks in advance,

Joke van de Plassche

_________________
Joke van de Plassche
Cognitive Science
University of Nijmegen
Montessorilaan 3
Postbus 9104
6500 HE Nijmegen
email: plassche@nici.kun.nl
Newsgroups: comp.ai
From: ab@Armstrong.EDU (Dr. Alex Bykat)
Subject: Re: CMU LISP Tutor
Organization: Armstrong State College, Savannah, GA
Date: Thu, 28 Jan 1993 13:33:36 GMT

Please post it to the group. I suspect ther are many others
that would like to know. (Including myself)

AB


                      ********************************************
                      *  Dr. A. Bykat                            *
                      *  Callaway Professor of Computer Science  *
                      *  Department of Math & Computer Science   *
                      *  Armstrong State College                 *
                      *  Savannah, GA 31419                      *
                      *  USA                                     *
                      *                                          *
                      *      Email:  ab@pirates.armstrong.edu    *
                      ********************************************
Newsgroups: comp.ai.fuzzy