HDTenEightyP/NetNews huggingface dataset:
From: ingvar@idt.unit.no (Ingvar Fagerland)
Subject: Request:Info about SW enviroment for special hardware
Reply-To: ingvar@idt.unit.no (Ingvar Fagerland)
Organization: Div. of CS & Telematics, Norwegian Institute of Technology
Date: Wed, 2 Sep 92 14:15:14 GMT



We are currently working on a project with a parallel computer
for simulation of neural networks. This involves developing a framework 
for controlling the simulation.

We are looking for information about software 
enviroments controlling ANN simulations on parallel hardware.
(e.g. articles, references, people working on similar projects.)

A typical example might be a client running on a workstation, controlling 
a ANN server running on special dedicated hardware.
We are interested in different strategies used for controlling the 
ANN simulation, and the interface between the control unit and 
the simulation unit ( if appropriate ).
( e.g. protocols used in the communication between client and 
server, what services is expected, etc.).

Software enviroments for simulation on conventional machines are 
also of interest. 


Please answer by email, We'll make a summary.

Thanks in advance!!

Ingvar Fagerland





Newsgroups: comp.ai.neural-nets
From: pfsum@cucs18.cs.cuhk.hk (Sum Pui Fai)
Subject: references on boltzmann/hopfield net in control
Organization: Faculty of Engineering, The Chinese U. of Hong Kong
Date: Sun, 30 Aug 1992 16:07:51 GMT


Would like to ask any netter who has the experience in implementation of
Hopfield Net or Boltzmann Machine in control engineering. Any reponse
can send to the following e-mail address 

e-mail : pfsum@cucs26.cs.cuhk.hk

If possible, a summary will be posted later on. Thanks in advance and
best regards.

John Sum 

Newsgroups: comp.ai.neural-nets
From: delliott@src.umd.edu (David L. Elliott)
Subject: Re: Neural-nets and (vs.) Statistical Methods
Summary: Repost of info from Brian Ripley
Organization: College of Engineering, University of Maryland, College Park
Distribution: Comp.ai.neural-nets
Date: Wed, 26 Aug 1992 22:34:30 GMT

Repost of info from Brian Ripley about his article on ANN vs Statistical methods
....................................
From: ripley@vax.oxford.ac.uk
Newsgroups: comp.ai.neural-nets
Subject: Paper available: `Statistical Aspects of Neural Networks'
Date: 3 Aug 92 14:06:44 GMT
Organization: Oxford University VAXcluster

A paper, with principal audience statisticians, entitled

	Statistical Aspects of Neural Networks

is available by anonymous ftp from 

	markov.stats.ox.ac.uk (192.76.20.1 or 129.67.1.190)

at pub/S/papers/ripley.ps.Z (336kB), with abstract ripley.abstract as
follows:

Neural networks have been a much-publicized topic of research in the
last five years, and are now beginning to be used in a wide range of
subject areas traditionally thought by statisticians to be their
domain. This paper explores the basic ideas of neural networks from the
point of view of a statistician, and compares some of their
applications with those of traditional and modern methods of statistics
and pattern recognition.

Neural networks are mainly used as non-linear approximations to
multivariable functions or as classifiers.  They are non-parametric in
character in that no subject-domain knowledge is incorporated in the
modelling process, and the parameters are estimated using algorithms
which at least in principle can be computed on loosely-coupled parallel
computers.  We argue that the modelling-based approach traditional in
statistics and pattern recognition can be at least as effective, and
often more so. This is illustrated by data on the areas in Zimbabwe
environmentally suitable for Tsetse flies.


Invited lectures for SemStat (S\'eminaire Europ\'een de
Statistique), Sandbjerg, Denmark, 25-30 April 1992. To appear in the
proceedings to be published by Chapman & Hall in January 1993.

.----------------------------------------------------.
|      Prof. Brian D. Ripley                         |
|      Dept. of Statistics,  University of Oxford,   |
|      1 South Parks Road,   Oxford OX1 3TG, UK      |
|                                                    |
|      ripley@uk.ac.ox.stats (JANET)                 |
|      ripley@stats.ox.ac.uk (Internet)              |
`----------------------------------------------------'


From disney.src.umd.edu!mojo.eng.umd.edu!darwin.sura.net!europa.asd.contel.com!uunet!mcsun!uknet!ox-prg!oxuniv!ripley Thu Aug  6 15:30:57 EDT 1992
Article: 7291 of comp.ai.neural-nets:
From: ripley@vax.oxford.ac.uk
Newsgroups: comp.ai.neural-nets
Subject: Correct reference for `Statistical Aspects of Neural Networks'
Date: 3 Aug 92 14:10:55 GMT
Organization: Oxford University VAXcluster

My apologies, the paper is at: pub/neural/papers/ripley.ps.Z

Brian Ripley


Newsgroups: comp.ai.neural-nets
From: kaykit@iti.gov.sg (Chan Kay Kit)
Subject: Information on OCR products needed
Reply-To: kaykit@iti.gov.sg
Organization: Information Technology Institute, National Computer Board, S'pore
Date: Mon, 31 Aug 1992 05:31:23 GMT

Hello everyone,

	There are many commercial OCR engines on the market right now for
reading printed text. I am aware of products like the Kurzweil K-5000 and
Discover 7320 Model 5 (from Xerox), OmniPage (from Caere Corp.), SAIC (from
Differential Processing System) and NestorReader.

	I wonder if anybody has done independent evaluation of any OCR
products, since such information would be useful to potential users. 
Specifically, I need answers to the following questions:


(i) name of product and company which produces it  

(ii) fonts and point sizes handled by product

(iii) whether the OCR engine exhibits true multifont and "multisize"
      capability i.e. is able to recognize text without the user
      having to specify in advance its font type and point size.

      If so, what is the accuracy over a variety of fonts and point sizes?

      If not, what is the accuracy over common fonts like Courier, Times
      Roman and Helvetica and point sizes from 8 to 24 points? 

(iv) What are the commercial OCR products, if any, that can
     1. recognise a page of text containing a mixture of fonts and point
	sizes at greater than 98% accuracy?

     2. recognise a page of text containing a single known font and a
	range of point sizes at greater than 99% accuracy?

(v) Are there any commercial OCR systems that accept noisy input or 
    text on shaded background eg. carbon and photostated/fax copies and
    still maintain an acceptable accuracy (say greater than 95%)?
  

Further, if there are any evaluations of commercial handwritten OCR systems, 
I will also be interested to hear about them.

Please email all replies/references to kaykit@iti.gov.sg. If there are 
enough responses, I will summarise.

Thanks!




---
Kay-Kit CHAN    			| Internet: kaykit@iti.gov.sg
Knowledge Systems Lab			| Bitnet:   kaykit@itivax
Information Technology Institute	| Tel:      (65) 772-0920
National Computer Board of Singapore    | Fax:      (65) 770-3043
From: hln@daimi.aau.dk (Henrik Lykke Nielsen)
Newsgroups: comp.ai.neural-nets
Subject: Recently posted article on -f stock-nn comp.ai.neural-nets
Date: 25 Aug 92 18:49:19 GMT
Organization: DAIMI: Computer Science Department, Aarhus University, Denmark


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
From: batra@boulder.Colorado.EDU (sajeev batra)
Newsgroups: comp.ai.neural-nets
Subject: Re: Travelling salesman problem: help needed
Date: 29 Aug 92 00:19:16 GMT
Organization: University of Colorado, Boulder


____

I did the exact same project for my neural nets class last year.
I used a constraint-satisfaction net with my own modified learning 
algorithm.  It wasn't very good, though.  But I still got a decent
grade on it.

Anyways, here are some references I have:

Lawler, E, Lenstra, J, Rinnooy Kan, A, and Shmoys, D "The Traveling  Salesman
Problem", Wiley, 1985.


Lister, R. Segment Reversal andd the TSP, IJCNN 1990, v 1, 424-7.

Lin/Kernighan , An Effective Heuristic Algo. for the TSP, Operations
Research  21, 498-516, 1973.

Kirkpatrick, S and Toulouse, G (1985) 'Configuration space analysis of
   travelling salesman problems' Journal de Physique 46, pp 1277-1292


There are many more.  Check out the book by Hertz/Krogh/Palmer "An
Intro. to the theory of neural computation."  It has a good descriptiioon
and many more references.



cheers,

sajeev.
From: guedalia@bimacs.BITNET (David Guedalia)
Newsgroups: comp.ai.neural-nets
Subject: Re: Intel 8017ONX analogue neural network chip. HELP!!!
Keywords: hardware 8017ONX analogue neural network trainable
Date: 27 Aug 92 14:30:02 GMT
Organization: Math department, Bar-Ilan University, Ramat-Gan, ISRAEL

In article <?.714767138@tazdevil> henrik@mpci.llnl.gov (Henrik Klagges) writes:
>srms@newt.phys.unsw.edu.au (Sergio Sandler) writes:
>
>>Somebody told me that INTEL is selling an electronically trainable
>
>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,

>PS: May very well be possible that they upgraded that chip by now.

   The Intel 80170nx is out ( I am not sure if that was the same chip
you read about).  It is NOT trainable as was mentioned.  Rather a
simulation trains and the weights are downloaded to the chip.  I think
I remember hearing that it is accurate to 3 places past the decimal
point.
  For more accurate information contact Finla Martin (Fin) at
email : fmartin@intel.sc9.com

  It has been used in some real life problems.

David
From: sethb@fid.morgan.com (Seth Breidbart)
Subject: Re: measuring complexity (was Re: Kolmogorov Complexity)
Organization: Morgan Stanley & Co., New York, NY
Date: Fri, 28 Aug 1992 16:49:41 GMT

In article <1992Aug27.181810.7249@cs.sfu.ca> jamie@cs.sfu.ca (Jamie Andrews) writes:
>In article <1992Aug27.010147.16422@fid.morgan.com> sethb@fid.morgan.com (Seth Breidbart) writes:
>|In article <1992Aug17.171934.9364@cs.sfu.ca> jamie@cs.sfu.ca
>|(Jamie Andrews) writes:

>     I suppose I was intending to mean something like this:  an
>epsilon-delta algorithm A which, when given a string S, outputs
>a program P such that
>(a) P outputs S, and
>(b) with probability (1-delta), the length of P is within
>    epsilon percent of the length of the minimal program which
>    outputs S.
>(It's trivial to output *some* program which outputs S -- it
>would be just (defun (result) (quote S)), where S is the listing
>of the string.  The problem is getting the length down, for long S.)

I assume that A is a random "algorithm".

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.

If you mean, for S a random string (with some probability), then the
trivial program you give will (almost) work:  for almost all strings,
the shortest program involves quoting the string.  Of course,
assigning probabilities to strings is highly non-trivial; that aspect
alone prevents this interpretation from being useful (if strings have
non-zero probability, then a finite number of them have total
probability > 1-delta).

>     This might not be the exact formulation needed... is this
>problem trivial too?  Do we need some tighter epsilon-delta
>bound?  What is the sample space -- all strings of length k
>or less, or all strings output by programs of length k or less,
>or something else?  What is the distribution on the sample
>space?  And what would the algorithms look like, and what would
>be their time and space bounds?  I think these are pretty
>interesting questions.

If you say "for all k, assigning equal probabilities to all strings of
length bounded by k and 0 to all others", then you avoid the last
problem I raise, but once again the trivial program works almost
everywhere (and a finite correction to it works everywhere).

Seth		sethb@fid.morgan.com
Newsgroups: comp.ai.neural-nets
From: dave@afs.com (David J. Anderson)
Subject: failsafe NNs
Date: Tue, 25 Aug 1992 15:46:40 GMT

Having looked at neural nets from the peripery for a while, I'm wondering  
if anyone has looked at NN from the angle of breaking down.

As an analogy, it seems to me that every so often I'm faced with a concept  
so radically new, so different, that a good deal of the training I had is  
no longer sure and a new training/exploration mode must be entered to get  
my wetnet back up to speed.

Granted, wetnets are a great deal more complex than the NNs we've created  
so far. But I beleive the analogy applies: what happens when the inputs to  
a NN are so weird that they cannot be adequately prepared for? If we start  
using NNs for `smarter' applications, and the penalty for being wrong gets  
nastier, how can we assure that the inputs to the NN can be responded to  
appropriately?

____________________________________________________________________
Dave Anderson  |    ...consequently, society expects all earnestly
  the dman     |    responsible communication to be crisply brief...
dave@afs.com   |    we are not seeking a license to ramble wordily.
Newsgroups: comp.ai.neural-nets
From: gsbg9079@uxa.cso.uiuc.edu (G. Scott Braley)
Subject: Blum's NN in C++
Organization: University of Illinois at Urbana
Date: Wed, 26 Aug 1992 22:21:44 GMT

If anyone has bought this book, you might be interested in getting a hold of
the corrected code.  I had someone at the bookstore where I purchased it call
the publisher, who later sent a disk with the correct code on it.  I haven't
tried it out yet, but you may want to have your bookstore call for you (or call
yourself, whatever :) 		

				Scott

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

Xref: sparky comp.ai.neural-nets:3366 comp.ai.philosophy:5930 sci.cognitive:385
From: erwin@trwacs.fp.trw.com (Harry Erwin)
Newsgroups: comp.ai.neural-nets,comp.ai.philosophy,sci.cognitive
Subject: Behavioral Neurodynamics Conference
Keywords: quantum field theory, non-linear dynamics
Date: 1 Sep 92 14:26:29 GMT
Followup-To: sci.cognitive
Organization: TRW Systems Division, Fairfax VA

I will be attending the First Appalachian Conference on Behavioral
Neurodynamics, and I will post a summary here afterwards.

Cheers,
-- 
Harry Erwin
Internet: erwin@trwacs.fp.trw.com
From: PVJQC@CUNYVM.BITNET
Newsgroups: comp.ai.neural-nets
Subject: RE: Applying NN to High speed ATM Networks
Date: 28 Aug 92 13:17:10 GMT
Organization: City University of New York/ University Computer Center

In article <1992Aug27.143405.27270@bmerh85.bnr.ca>, carrasco@bmers52.bnr.ca
(Sylvano Carrasco) says:
>
>Prashant Joshi mentioned that he had found one paper on applying neural-nets to
>ATM networks.  Could you please post that reference.  I would also appreciate
>any information on NN ATM applications
  I am posting the reference as per Sylvano Carrasco's request.
It is a paper in the proceedings of Globecom '91 pages 0115-0119 by
Xiaoqiang Chen and Ian M. Leslie, Uni of Cambridge, UK.

   Sylvano, if you have any more information on Traffic management for
ATM networks from `what more needs to be done' standpoint, please let me
know.
-----
-------

         *********************************************************
         *  PRASHANT V. JOSHI                                    *
         *  COMPUTER SCIENCE DEPARTMENT                          *
         *  CITY UNIVERSITY OF NEW YORK.                         *
         *  INTERNET ADDRESS: PVJQC@CUNYVM.CUNY.EDU,             *
         *  BITNET ADDRESS  : PRASHANT@QCVAX.BITNET              *
         *                                                       *
         *********************************************************
Newsgroups: comp.ai.neural-nets
From: int602k@mdw030.cc.monash.edu.au (Ms Lai Hooi Yee)
Subject: hopfield source for classification
Organization: Monash University
Date: Mon, 31 Aug 1992 02:53:27 GMT


Hello everyone! :)

	Could anyone please give me a brief explanation on hopfield network and
self-organising network? Is there any source codes available for classification
purposes?

	Thanks a lot. I really appreciate it. Thanks x 1000...

cheers,
kelie

P/S please email any responses to kelie@bruce.cs.monash.edu.au
From: david@tls-cs.cert.fr (Vincent David)
Newsgroups: comp.ai.neural-nets
Subject: test
Date: 3 Sep 92 10:29:20 GMT
Organization: CERT Toulouse Dept of Computer Science, FRANCE


  this is a test.
From: swarnama@iti.gov.sg (S. Swarnamala)
Newsgroups: comp.ai.neural-nets
Subject: Clustering Algorithms - some basic issues
Date: 27 Aug 92 04:39:29 GMT
Reply-To: swarnama@iti.gov.sg
Organization: Information Technology Institute, National Computer Board, S'pore

Hello fellow netters,
I am trying to use RBF-based hybrid network for prediction. I follow the design as is given
 in literature - Single hidden layer feed forward BP-net with linear output nodes and 
guassian hidden nodes. I also use additional nodes each of input dimension for every 
hidden unit (intruduced for improving prediction). I have simulated this net using around 
100 guassians for a 12 dimensional input of around 400 input sample size 
(could be oversized - I don't know). 

I have some fundamental questions regarding this :

 1) The unsupervised part of learning in this context is basically a process of clustering 
all the input patterns of N-dimension. The common way of quantifying the tightness of
 clustering is sum((Eu)dist of each pattern from its closest cluster center). What should 
this measure be for a good clustering?  OR in other words " Is there a bench mark against
 which the quality for a given clustered sample could be compared ( such as Average Relative
 Variance for prediction)"

 2) Is there any empirical relation between no: of attractors (cluster centres), input
                           --------
 pattern dimension and no: of patterns to be clustered ?

 3) Any figures of reasonable input dimension for which RBF network size will be manageble ?

I request for answers to these queries. Also am thankful for any pointers to literature
 discussing these issues . I shall summarise the replies I get on this news-group.

-----------------------------------------
 Srirengan Swarnamala,                       
 Knowledge Systems Lab,
 Information Technology Institute,
 National Computer Board of Singapore.
 (E-mail : (Internet) swarnama@iti.gov.sg
 
Newsgroups: comp.ai.neural-nets
From: hutchens@cod.nosc.mil (Chris G. Hutchens)
Subject: Seeking neural-net hardware design position.
Organization: Naval Ocean Systems Center, San Diego
Distribution: usa
Date: Wed, 2 Sep 1992 23:10:31 GMT

From hutchens@cod.nosc.mil Wed Sep  2 16:03:36 1992
Received: from cpc-7.nosc.mil by cod.nosc.mil (5.65/1.34)
	id AA02147; Wed, 2 Sep 92 16:03:31 -0700
Date: Wed, 2 Sep 92 16:03:31 -0700
From: hutchens@cod.nosc.mil (Chris G. Hutchens)
Message-Id: <9209022303.AA02147@cod.nosc.mil>
To: hutchens
Status: O

Hello:

     I am presently in job market with neural-net
hardware design background.  This field being limited to
research territories, I am having a problem finding
suitable employment that will justify my experience and
interest.  Following is the quick summery of my research
background.

     Currently I am working as a analog neural-net IC
designer at Naval Command, Control and Ocean Surveillance
Center (NRaD), San Diego.  We develop neural network
algorithms that suit our applications and our hardware.
Currently we are implementing olfactory neural algorithm
in bulk CMOS analog integrated circuitry.  My work at
NRaD involves design of integrated circuits, SPICE
simulations, layouts, and testing of neural network
building blocks.  We are also developing the floating
gate-memory in standard CMOS process which takes
advantage of mask geometries to cause field enhanced
Fowler-Northeim tunneling of the electrons at relatively
low programming voltages.

     Should you be aware or become aware of any of your
business associates, friends or acquaintances who may be
in the market for someone with my background and
abilities, I would truly appreciate their names to
contact personally.

Sanjay B. Patil
3845 La Cresta Drive
San Diego, CA 92107

Phone
Work: (619) 553-2680
Home: (619) 222-2371
E-mail: hutchens@cod.nosc.milTo: hutchens
Subject:
Cc:
Text:




Newsgroups: comp.ai.neural-nets
From: ddoherty@ics.uci.edu (Donald Doherty)
Subject: Re: Smell in Neural Network
Reply-To: ddoherty@ics.uci.edu (Donald Doherty)
Organization: Univ. of Calif., Irvine, Info. & Computer Sci. Dept.
Date: Fri, 28 Aug 1992 01:00:35 GMT

In article <1992Aug18.115759.21078@csd.brispoly.ac.uk> c_tsai@csd.brispoly.ac.uk (C Tsai) writes:
>   Who knows 'smell' in Neural Network????  As I know that now only some people
>research 'Speech' or 'Image Processing' in Neural Network.  Why nobody research
>about 'Smell' in Neural Network!!!!

As was recently mentioned on the net, there are many researchers who have
simulated and built models of various parts of the olfactory system.

Off the top of my head:

	Walter Freeman
	Gary Lynch
	Rick Granger
	Jim Bower

and there are *many* others.  There is even a book published this year
devoted entirely to olfaction and neural nets.

Don

-------------------------------------------------------------------------------
Donald Doherty
Dept. of Psychobiology		Email:	    ddoherty@ics.uci.edu
University of California	CompuServe: 76646,1321
Irvine, CA  92717-4550		FAX:	    (714) 725-2447
U.S.A.				Voice:	    (714) 856-1776

Newsgroups: comp.ai.neural-nets,comp.ai,comp.theory
From: jamie@cs.sfu.ca (Jamie Andrews)
Subject: Re: measuring complexity (was Re: Kolmogorov Complexity)
Organization: CSS, Simon Fraser University, Burnaby, B.C., Canada
Date: Mon, 31 Aug 1992 21:17:53 GMT

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.

     What do you mean by a "probabilistic solution"?  It won't
solve the busy beaver problem, it will just give an approximate
solution.  If we set epsilon and delta to 0, presumably the
algorithm will no longer terminate.

>If you say "for all k, assigning equal probabilities to all strings of
>length bounded by k and 0 to all others", then you avoid the last
>problem I raise, but once again the trivial program works almost
>everywhere (and a finite correction to it works everywhere).

     That's why I suggested something which you seem to have
skipped over completely, namely that we assign equal
probabilities to all strings generated by programs of length k
or less.

     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 :-)).

--Jamie.
  jamie@cs.sfu.ca
"Every \item command in item_list must have an optional argument." LaTeX pg.168
From: naik@constitution.rutgers.edu (Devang Naik)
Newsgroups: comp.ai.neural-nets
Subject: Matlab source code for backpropagation algorithm
Keywords: matlab, mlps, feedforward, gradient descent
Date: 3 Sep 92 19:16:40 GMT
Article-I.D.: constitu.Sep.3.15.16.40.1992.6047
Organization: Rutgers Univ., New Brunswick, N.J.


Hello netter,

	A couple of months back, someone posted a matlab code for
	the backpropagation algorithm on the net. I would appreciate
	if someone could send me the source code. Thanks a lot in
	advance, 

	Devang Naik,
	CN 1390, CAIP center,
	NJ 08855
Newsgroups: comp.ai.neural-nets