HDTenEightyP/NetNews huggingface dataset:
From: alex@maxc.technion.ac.il
Subject: Schnorrenberg Neocognitron Simulator ?
Organization: 
Date: Thu, 27 Aug 1992 12:10:25 GMT

Hello,
Is anybody experienced with Neocognitron Simulator of F.Schnorrenberg? If 
yes, please could you answer a few questions:

1. How to obtain during the training process the necessary set of weights, 
matching for classification the given set of patterns. What is the syntax 
of this command?

2. How to evoke the simulator ONLY to classify (based on the given set of 
weights) the given set of patterns, WITHOUT ANY UPDATING of the weights 
(i.e.training)?

3. How to explain the screen output of the Simulator, specifically, what  
does it mean that he  finds the winners in every stage in the beginning of 
the training process. Known, that self - organization of the Neocognitron 
progresses step by step from the front layer to the last and no need to 
propagate the input pattern from the beginning to the end before training 
is completed. What do the huge arrays that the Simulator prints out mean? 
The weights?

Does anybody know another public domain simulator of the Neocognitron 
(under MS-DOS,Windows, X11, ets) more friendly to use? All the advises, 
references etc will be thankfully appreciated. Please respond directly or 
into the net-news group ai.neural. The responses will be summarized and 
posted. Thanks in advance,

Alex Beylin
Dept. of Bio-Med. Eng., Technion,
Haifa, Israel
From: jagota@sybil.cs.Buffalo.EDU (Arun Jagota)
Newsgroups: comp.ai.neural-nets
Subject: seeking refs: vertex cover using NNs
Date: 28 Aug 92 22:35:17 GMT
Organization: State University of New York at Buffalo/Comp Sci
Originator: jagota@sybil.cs.Buffalo.EDU

I am seeking references on application of neural networks to the vertex
cover (MVC) graph optimization problem. I am aware of one encoding of MVC (
TR208/88; U. Toronto; 1988) in a Hopfield net; though this encoding is only
for complexity theory purposes. Scanning the TOC of the book: 
Neural Networks for Parallel Computing, Y. Takefuji does not show MVC was
addressed. Also, vertex cover is apparently mentioned only in one line in the 
book: Simulated Annealing and Boltzmann Machines - A Stochastic Approach to
Combinatorial Optimization and Neural Computing, E. Aarts & J. Korst. 
One reason may be that MVC may be indirectly solved (approximated) via 
approximation to maximum clique, maximum independent set, etc. I have 
references to these; I am looking for _direct_ solution of MVC. 
   Please e-mail references directly; encoding details would be even more
appreciated. I will e-mail out what I collect upon request.
Thanks in advance,  Arun Jagota     jagota@cs.buffalo.edu
From: henrik@mpci.llnl.gov (Henrik Klagges)
Newsgroups: comp.ai.neural-nets
Subject: Re: Intel 8017ONX analogue neural network chip. HELP!!!
Keywords: hardware 8017ONX analogue neural network trainable
Date: 25 Aug 92 18:25:38 GMT

srms@newt.phys.unsw.edu.au (Sergio Sandler) writes:

>Somebody told me that INTEL is selling an electronically trainable
>analogue neural network chip no. 8017ONX, having 64 neurons. If this
>information is correct, could somebody tell how could I get that chip,

I looked at the specs of the 80170 about a year ago. As far as I remem-
ber, it is NOT trainable in the classical fashion. If I remember correctly,
it is weight-burn in and forget - so it essentially always runs in production
mode. (they may have an eeprom-like reprogramming feature, but if, it is not
fast enough for online learning). 
Apart of that, the numerical precision obtainable with the resistor array is
not too exciting. Last I heard was that manufacturing tolerances allowed for
a resolution of 256 values at best for stored parameters (e.g., weights), and
monotonicity couldn't be guaranteed. 

Cheers, Henrik
Massively parallel group at Lawrence Livermore
IBM Research division physics group Munich

PS: May very well be possible that they upgraded that chip by now.
Newsgroups: comp.ai.neural-nets,comp.ai,comp.theory
From: sethb@fid.morgan.com (Seth Breidbart)
Subject: Re: measuring complexity (was Re: Kolmogorov Complexity)
Organization: my opinions only
Date: Thu, 27 Aug 1992 01:01:47 GMT

In article <1992Aug17.171934.9364@cs.sfu.ca> jamie@cs.sfu.ca
(Jamie Andrews) writes:

>     So, for instance, the K-complexity of a given list of
>numbers might be defined as:  the length of the shortest program
>in pure LISP such that the value of the s-expression "(result)"
>is that list.  (Well, I guess it would have to be pure LISP plus
>some arithmetic functions.)  The "length" of a program might be
>defined as the total number of atoms plus the total number of
>sets of parentheses, to give a more realistic idea of the
>storage space needed by the program.

If any number will fit in one atom, then all strings have complexity
bounded above by some small number.

>     A measure of K-complexity relative to some standard suite
>of function definitions could be given, too.  The relative
>K-complexity would be the length of the shortest pure LISP
>program containing that suite of definitions, _minus_ the length
>of those definitions.  The minimal program could then make use
>of the "knowledge" in the standard functions without having to
>derive it.

Since the answer is almost always undecidable, I doubt you could get
any interesting results.

>     By the way, I asked about K-complexity here a few years
>ago, and was given convincing arguments that finding the
>K-complexity of a given string is in general undecidable.

Since no string can be shown to have complexity greater than C (for
some constant C), and all except a finite number of strings have
complexity greather than C, ...

>I don't know about randomized algorithms, though.

If a randomized algorithm is any that _can_ output the string, a
trivial one exists: (output random bit; maybe loop).  If it _must_
output the string, or can only output the string (or die), or outputs
the string with some minimal probability, then you haven't gained
anything from randomness.

Seth		sethb@fid.morgan.com
From: Monroe.Thomas@ttlg.UUCP (Monroe Thomas)
Sender: postmaster@ttlg.UUCP
Newsgroups: comp.ai.neural-nets
Subject: Noise Recognition
Date: 27 Aug 92  15:23:08 mst

PP>I'm trying to set up a project on noise recognition systems, recognize some
  >form of noise such as siren (police, fire etc.), alarm signal or any other
  >warning noise which will alert a deaf person.

PP>I'm thinking of using Signal Processing and Neural Network for detecting
  >above noise and will be using parallel processing such as Transputers
  >and DSP (perhaps, I will be using Transputers instead of DSP).

PP>I would be grateful if you had any ideas/info' of using Signal Processing
  >and Neural Network (both hardware and software) for the above project - any
  >hints would be grateful obtained.

I think that ALNs are ideally suited to this problem.  To get the
latest ALN software, use anonymous FTP from menaik.cs.ualberta.ca.

Here is a typical session with ftp, aimed at retrieving the adaptive
logic network (ALN) software. Unix prompts are "%", ftp prompts are "ftp>" and
editorial comments are in "[]".
% ftp      [start ftp]
ftp> open menaik.cs.ualberta.ca       [or "open 129.128.4.241"]
Name (menaik.cs.ualberta.ca:arms): anonymous
Password: [type your login id here, if you like]
ftp> cd pub
ftp> type binary [ we are dealing with a compressed file]
ftp> get atre27.exe
ftp> quit

Now run this program on your PC (or SPARCstation running Microsoft
Windows). The program will ask permission to create directories and
extract files into them.  Running setup gives you icons to click on.

Similarly for atree.tar.Z (for Unix)
atree2.ps.Z (a compressed PostScript document),
alnlpst-1991.Z (the alnl mailing list archive for 1991)] and
a27exe.exe (the executables of atree release 2.7).

Have fun!

-Monroe

 * OLX 2.2 * "The male sheep was cut badly," Tom rambled.

 * Origin: Through the Looking Glass (42:100/14)
Newsgroups: comp.ai.neural-nets
From: bminch@grate.ugcs.caltech.edu (Bradley Arthur Minch)
Subject: Neural-nets and (vs.) Statistical Methods
Organization: Computation and Neural Systems, Caltech
Distribution: Comp.ai.neural-nets
Date: Wed, 26 Aug 1992 16:59:44 GMT

Hello all,

	A few weeks ago, someone posted a blurb about a paper by a statistician
who compared applying neural-nets to some statistical methods available via
anonymous ftp.  I wrote down the pointer and subsequently misplaced it.  Could
someone kindly direct me back there.  Responses via e-mail or by posting to the
net are fine.

					Regards,
					Brad Minch
					bminch@hobiecat.pcmp.caltech.edu
From: duncan@spd.eee.strathclyde.ac.uk (Duncan C Thomson)
Newsgroups: comp.ai.neural-nets
Subject: Re: Looking for EEG/EMG data
Keywords: EEG/EMG data
Date: 3 Sep 92 13:02:36 GMT
Organization: SPD, Strathclyde University, Glasgow, Scotland
Originator: duncan@ossian

In comp.ai.neural-nets, arkell@swanee.ee.uwa.edu.au (Arkell Rasiah) writes:
>
>Hi everyone,
>	I am looking for EEG, EMG or ECG data to work with. Can someone tell
> me where I can download such data from or even purchase such data (eg. on
> CDROM).

Here in the Signal Processing Division we have the MIT-BIH arhythmia database,
which has around 24 hours of two-lead annotated data.  This database is on
CD-ROM, and costs about USD 1000.  Contact:

	George B Moody
	MIT Room 20A-113
	Cambridge, MA 02139
	USA

Hope this is of help, I would be interested to know of any other ECG databases
available,

	-Duncan

-- 
Duncan   <duncan@spd.eee.strathclyde.ac.uk> |  .__ _. _ .__._  _ , .___ _
Thomson     (OR <cnbr50@vaxa.strath.ac.uk>) |  |_ |_ |_||_ |_|/_\|\| | / \
SPD, Dept of EEE, University of Strathclyde |  |__._||  |__| \| || | | \_/
204 George Street, Glasgow G1 1XW, UK       | esperanto - lingvo internacia
From: kavuri@lips.ecn.purdue.edu (Surya N Kavuri )
Newsgroups: comp.ai.neural-nets
Subject: Re: Network Inversion
Date: 23 Aug 92 18:10:41 GMT
Organization: Purdue University Engineering Computer Network

In article <arms.714522787@spedden>, arms@cs.UAlberta.CA (Bill Armstrong) writes:
> tjochem+@CS.CMU.EDU (Todd Jochem) writes:
> 
> >I'm looking for references to network inversion papers. The basic idea
> >I'm interested in is presenting the network's output with a signal and
> >by back-propagating this signal though the layer(s), recreating an input
> >which could have created the applied output signal.
> 
> This is intractable for a general multi-layer perceptron.
> 
> Proof: We have to show it for the special case of ALNs, which are
> trees of nodes realizing AND, OR, LEFT and RIGHT functions, with
> leaves connected to input bits and complements.  The same input bit or
> complement may be sent to many leaves.  It will be enough to show it
> for a two-layer tree, with ORs feeding into an AND.  Back-propagating
> a 1 signal is just CNF-satisfiability, and is NP-complete.
> 
> Comment: the difficulty arises because back-propagating signals often
> converge at the same node, either at the inputs (ALNs) or in hidden
> layers (MLPs), and they are likely to have contradictory values when
> they do.
> 
> --
> ***************************************************
> Prof. William W. Armstrong, Computing Science Dept.

 May be there are easier ways of explaining this :-)


  
 Inversion of a network may not return the exact input (that was 
 perhaps used in the training) for an MLP.  This is because the
 function developed by the network may not be invertible.  This
 of course means that there are many possible inputs that 
 can give you the same output.  Use gradient descent search to 
 find any one of them or use genetic type multiple point 
 search techniques to find more than one.
 In general, the input you determined may not give the exact 
 ouput you have (may be a local minima or may be the output 
 can't be generated by your network).  So, in general you 
have no guarantee to find the inversion.

Yo can see this another way:
Since each node maps many inputs to a single value, you may be 
able to find many inputs which give you the same node value.






Surya Kavuri
Newsgroups: comp.ai.neural-nets
From: pjw@col.hp.com (Peter Walsh)
Subject: Re: Recently posted article on -f stock-nn comp.ai.neural-nets
Date: Wed, 26 Aug 1992 19:29:17 GMT
Organization: HP Colorado Springs Division

hln@daimi.aau.dk (Henrik Lykke Nielsen) writes:
> 
> A news concerning "neural-nets and stocks" has 
> recently been posted.
> 
> I think it was a summary...
> 
> Could somebody please e-mail me this news...
> 
> thanks
> 
> -henrik
> 
> E-mail: hln@daimi.aau.dk

I too am very much interested in the above mentioned summary!

Peter Walsh
pjw@col.hp.com
Newsgroups: comp.ai.neural-nets
From: jyothi@franklin.ee.umr.edu (Jyothi Nagaraj)
Subject: Opportunities
Date: Fri, 4 Sep 1992 18:21:37 GMT
Reply-To: jyothi@ee.umr.edu (Jyothi Nagaraj)
Organization: University of Missouri - Rolla
Originator: jyothi@ee.umr.edu



Hi!

Could somebody tell me about the job opportunities
in the industry in the area of Neural Network applications? 
How good is it right now?

Thanks in advance/

Jyothi



-- 
 -----
 Jyothi Nagaraj 		     email			Tel #
 210 EE Bldg, UMR,		jyothi@ee.umr.edu	H - (314) 341 8185
 Rolla,  MO 65401		jyothi@cs.umr.edu	O - (314) 341 4528
Newsgroups: comp.ai.neural-nets
From: Herman.Woltring@bbs.oit.unc.edu (Herman Woltring)
Subject: Re: Address wanted
Summary: netfind utility at Colorado University
Keywords:  netfind
Organization: Extended Bulletin Board Service
Date: Wed, 26 Aug 1992 00:15:19 GMT

In article <1913.2A9925EE@catpe.alt.za>
Steve.Hayes@p0.f20.n7101.z5.fidonet.org (Steve Hayes) writes:
 
> Can anyone give me an e-mail address for Dr Peter Cooley, of Aston
> University Birmingham, England?  He is working on genealogy programs
> making use of the principles of neural networks.
> Is anyone else working in that field?
>
> Messages to Postmaster@aston.ac.uk have brought no response.
> 
>... Steve Hayes, Editorial Dept, University of South Africa, Pretoria
>--  
>INTERNET: Steve.Hayes@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.


Via telnet to netfind@bruno.cs.colorado.edu (try it out, it's useful!),
the following addresses with email lastly read yesterday were found:
 
   cooleyp@sparc1.aston.ac.uk,  cooleyp@ipc2.aston.ac.uk

plus some accounts without email and never logged on to.
 
 
Herman J. Woltring, Eindhoven/NL <woltring@nici.kun.nl>
   (do not use the bbs address for private replies)
--
   The opinions expressed are not necessarily those of the University of
     North Carolina at Chapel Hill, the Campus Office for Information
        Technology, or the Experimental Bulletin Board Service.
           internet:  bbs.oit.unc.edu or 152.2.22.80
Newsgroups: comp.ai.neural-nets
From: pollack@dendrite.cis.ohio-state.edu (Jordan B Pollack)
Subject: Re: Upcoming Neural Net Conferences (Monthly Posting)
Originator: pollack@dendrite.cis.ohio-state.edu
Reply-To: pollack@cis.ohio-state.edu
Organization: Ohio State Computer Science
Date: Fri, 28 Aug 1992 14:32:21 GMT


Hmm, if one is going to simultaneously submit to these two meetings:

   Weightless Neural Network Workshop '93
	6-7 Apr 93	York, UK		31 Jan 93

   Annual Goddard Conference on Space Applications of Artificial Intelligence
	10-14 May 93	Greenbelt, MD, USA	1 Sep 92
   "Neural networks; must describe applicability to space-related
   problems"

perhaps the paper should be of little gravity :)



-- 
Jordan Pollack                            Assistant Professor
CIS Dept/OSU                              Laboratory for AI Research
2036 Neil Ave                             Email: pollack@cis.ohio-state.edu
Columbus, OH 43210                        Phone: (614)292-4890 (then * to fax)
From: Steve.Hayes@p0.f20.n7101.z5.fidonet.org (Steve Hayes)
Newsgroups: comp.ai.neural-nets
Subject: Address wanted
Date: Tue, 01 Sep 92 05:46:00 GMT
Organization: FidoNet node 5:7101/20.0 - Unisa Editorial, Pretoria

On 08-30-92  09:33 Herman Woltring said to All

 HW> Via telnet to netfind@bruno.cs.colorado.edu (try it out, it's
 HW> useful!), the following addresses with email lastly read yesterday were
 HW> found: 
 HW> cooleyp@sparc1.aston.ac.uk,  cooleyp@ipc2.aston.ac.uk
 HW> 
 HW> plus some accounts without email and never logged on to.

Thanks very much for that information.

 
... Steve Hayes, Editorial Dept, University of South Africa, Pretoria
--  
INTERNET: Steve.Hayes@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: ahg@eng.cam.ac.uk (A. H. Gee)
Newsgroups: comp.ai.neural-nets
Subject: Combinatorial optimization using NNs
Date: 4 Sep 92 15:07:32 GMT
Organization: Cambridge University Engineering Department, UK

There have recently been several requests on this newsgroup for
information concerning the solution of the TSP using feedback neural
networks. At the Cambridge University Engineering Department we have
been looking at the use of neural networks to solve combinatorial
optimization problems for some time. Our aim has been to impose a
degree of analytical rigour on what has tended to be a rather ad hoc
subject. Relevant reports are available in compressed postscript
format by anonymous ftp from our archive. The procedure for obtaining
a report is as follows:

unix> ftp svr-ftp.eng.cam.ac.uk
Name: anonymous
Password: (your email address)
ftp> cd reports
ftp> binary
ftp> get filename.ps.Z
ftp> quit
unix> uncompress filename.ps.Z
unix> lpr filename.ps (or however you print postscript)

The papers on combinatorial optimization are

aiyer_tr55.ps.Z   gee_tr62.ps.Z   gee_tr100.ps.Z
aiyer_tr60.ps.Z   gee_tr77.ps.Z
aiyer_tr89.ps.Z   gee_tr95.ps.Z

The directory also contains an INDEX, giving brief descriptions of
available papers and the email addresses of the authors, who would
welcome comments and suggestions from readers. As well as the work on
combinatorial optimization, there are several other reports by members
of the Speech, Vision and Robotics Group, many of which have a
significant neural networks content.

----------------------------------------------------------------------
Andrew Gee,                                   Email: ahg@eng.cam.ac.uk
Speech, Vision and Robotics Group,            Tel: +44 223 332754
Cambridge University Engineering Department,  Fax: +44 223 332662
Trumpington Street,
Cambridge CB2 1PZ,
United Kingdom.
From: v054smwl@ubvmsd.cc.buffalo.edu
Newsgroups: comp.ai.neural-nets
Subject: BP implementation in PDP:  IS IT BINARY OR ANALOG
Date: 30 Aug 92 03:23:00 GMT
Organization: University at Buffalo
News-Software: VAX/VMS VNEWS 1.41

I have copy of the book:

   Exploration in PDP  by Rumelhart (1989).  It comes with a floppy that 
has an implementation of the Back prop.  Now, this might sound like a silly
question, but it sure does drive me nuts:

   Is this implementation Analog or Binary.  And how would I find that out ?!!

In the text, sometimes it sound Analog, sometimes Binary.  I did use it for
few examples of my own which are Analog and it did work fine ( that might
be because those examples can also be formulated as binary ).  It did not
work at all with other Analog problems I tried to use it for .



  ANY HELP IS DEEEEEEEPLY APPRECIATED

elkordy
From: y_jin@csd.brispoly.ac.uk (Y Jin)
Newsgroups: comp.ai.neural-nets
Subject: Help: Looking for a book by K.S.Narendra
Date: 27 Aug 92 11:54:21 GMT
Organization: Bristol Polytechnic, England

Hi, Colleagues there:

I saw a workshop annoncement in IEEE Control Systems Magazine Vol.11, No.3,
1991. It said "The workshop will follow a forthcoming monograph "Neural Net-
works in Dynamical Systems" by K.S.Narendra". Does anyone there know whether
the book has be published? Could you please e.mail me the ISBN number if it has?

Thank you in advance.

Yichuang Jin (y_jin@csd.brispoly.ac.uk)
Xref: sparky comp.ai.neural-nets:3357 sci.math:10727 comp.theory:1841
From: lnd@cs.bu.edu (Levin)
Newsgroups: comp.ai.neural-nets,sci.math,comp.theory
Subject: Re: Kolmogorov's theorem
Date: 31 Aug 92 14:13:54 GMT
Reply-To: Lnd@cs.bu.edu
Followup-To: comp.ai.neural-nets

In article <BtuMKD.14p@acsu.buffalo.edu> ambati@ (Balamural K. Ambati) writes:
>Could someone post some info. on Kolmogorov's theorem on reconstruction
>of multidimensional functions and information processing in neural
>networks?  Also refs. if possible. Thanks.

Every continuous multivariate real function can be represented as a depth-4
composition of continuous one-variate functions and sum. This result of
Kolmogorov and his student Arnold solves the 13-th Hilbert problem.

 A.N.Kolmogorov. Soviet Math. Doklady (DAN SSSR) 108(2), 1956.
 V.I.Arnold. Same 114(4), 1957.
 A.N.Kolmogorov. Same 114(5), 1957.
Xref: sparky comp.ai.neural-nets:3336 news.answers:2642
From: frank@Cookie.secapl.com (Frank Adams)
Subject: Re: measuring complexity (was Re: Kolmogorov Complexity)
Date: Tue, 01 Sep 1992 22:18:08 GMT
Organization: Security APL, Inc.

In article <1992Aug31.211753.13335@cs.sfu.ca> jamie@cs.sfu.ca (Jamie Andrews) writes:
>In article <1992Aug28.164941.2028@fid.morgan.com> sethb@fid.morgan.com (Seth Breidbart) writes:
>>What do you mean by probability?  If you mean, for any string S, with
>>probability > (1-delta) P is within epsilon of the shortest program
>>for S, then A will lead to a probabilistic solution to the busy beaver
>>problem.
>
For any given problem X, there is a number K and a probability psi>0, such
that for any length N > K, the probability that a string S of length N is a
program easily recognizable as having a halting problem equivalent to X, is
greater than psi.  So if X is an unsolvable problem, you will never be able
to get delta less than psi.

>     I'm just throwing out ideas, really, trying to come up with
>a formulation of a problem that is interesting and non-trivial.
>You're welcome to your view that the problems I've stated so far
>are trivial, but that doesn't convince me that the whole area is
>trivial (though it may convince me to stop talking about it on
>the net :-)).

In general, this kind of problem does not admit of *any* sort of general
solution.  You may learn something by trying things and figuring out why
they don't work, but don't expect to find anything.
From: ak45ldp@Texaco.com (Larry D. Pyeatt)
Subject: Neural Networks and Genetic Algorithms	
Organization: Texaco
Date: Thu, 3 Sep 1992 13:22:46 GMT

Has anyone tried to use GA to find a NN architecture or
weight set?  Any information would be appreciated.

-- 
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.
Newsgroups: comp.ai.neural-nets,comp.ai,comp.theory
From: okellogg@cubenet.sub.org ( )
Newsgroups: comp.ai.neural-nets
Subject: NNs in natural language recognition?
Keywords: single-word (non-continuous speech) recog., human voice analysis
Date: 30 Aug 92 19:40:04 GMT
Distribution: comp


I'd be happy for any pointers to literature on Neural Nets in
(non-continuous, i.e. single-word) speech recognition, particularly as
applied to Esperanto, Spanish, Italian, or other languages with a
relatively simple phonetic system. I'm particularly interested in
network-training strategies used, as well as any progress concerning size
of the recognized vocabulary.

Thank you for your help.


--
Oliver M. Kellogg, Software Eng'r, DASA/MBB Space Systems, Munich, FRG
email:  okellogg@cubenet.sub.org
"I am the ultimate cultural masochist. I speak Esperanto and program in Ada."
Newsgroups: comp.ai.nlang-know-rep