HDTenEightyP/NetNews huggingface dataset:
From: jkn@ohm.york.ac.uk (John Nicoll)
Newsgroups: comp.ai.neural-nets
Subject: info/refs sought: CAUSAL-nets (inc. software)
Date: 26 Jan 93 18:10:04 GMT
Organization: Electronics Department, University of York, UK

Hello all
	I'm looking for information (for a colleague) on
`causal-nets' (whatever they are). I believe that there is a
(Dutch?) program called HUGIN for generating them, and that some
work has been done at Edinburgh AI applications Institute,
and perhaps at Cambridge (UK). beyond this my leads begin
to run out.

Any information beyond the above very gratefully received.
Email is preferable - I will sumarrise if there is any interest
(or suggestions for better newsgroups ;-)

	thank you
		john nicoll

-- 
john nicoll (jkn@ohm.york.ac.uk)
Department of Electronics  University of York
Heslington   YORK YO1 5DD U.K
Tel +44 (904) 433221   Fax +44 (904) 432335
Newsgroups: comp.ai.neural-nets
From: tstanley@lamar.ColoState.EDU (Thomas R Stanley)
Subject: Help, statistics and learning algorithms
Date: Tue, 26 Jan 1993 20:57:06 GMT
Organization: Colorado State University, Fort Collins, CO  80523

Hi everybody:

I am currently using classification procedures like Linear Discriminant
Analysis (LDA) and Classification And Regression Trees (CART) as model
selection procedures for a known (finite) set of multinomial models.  In
general, I generate data under a known model using Monte Carlo
simulations, compute diagnostic statistics from the simulated data, then
use these statistics as training data for LDA and CART.  Thus, given a set
of new data where the underlying model is unknown, I compute the
diagnostic statistics and let LDA and CART tell me which model the new
data were most likely to have come from.  Lately, I have become interested
in using neural nets for model selection under a supervised learning
algorithm.  My question is, what are the statistical equivalents of the
supervised learning methods commonly used in constructing neural nets? 
More precisely, are there statistical equivalents to the following (see,
I did check the FAQ first :) learning methods:

2. SUPERVISED LEARNING (i.e. with a "teacher"):
       1). Feedback Nets:
           a). Brain-State-in-a-Box (BSB)
           b). Fuzzy Cognitive Map (FCM)
           c). Boltzmann Machine (BM)
           d). Mean Field Annealing (MFT)
           e). Recurrent Cascade Correlation (RCC)
           f). Learning Vector Quantization (LVQ)
       2). Feedforward-only Nets:
           a). Perceptron
           b). Adaline, Madaline
           c). Backpropagation (BP)
           d). Cauchy Machine (CM)
           e). Adaptive Heuristic Critic (AHC)
           f). Time Delay Neural Network (TDNN)
           g). Associative Reward Penalty (ARP)
           h). Avalanche Matched Filter (AMF)
           i). Backpercolation (Perc)
           j). Artmap
           k). Adaptive Logic Network (ALN)
           l). Cascade Correlation (CasCor)

I found one reference that said BP was equivalent to least squares
fitting.  Is this true of the rest of the methods?  If these methods are
really just variations on classical statistical procedures, then (and here
is where I show my ignorance) what do I have to gain by using neural nets? 
Why should I expect these procedures to perform better than a parametric
method (let's assume for the sake of argument the assumptions of the
method (e.g. normality) are met) where there exists maximum likelihood
estimators (MLE's) for the parameters (i.e. weights at the processing
element or node)?  MLE's guarantee unbiasedness and efficiency (minimum
variance), you can't get better than that can you?  I would appreciate any
help, ideas, or input I can get on these questions.  I don't want to spend
a lot of time looking into neural nets if they are unlikely to lead to a
better model selection procedure.  Thanks in advance.

                                          Tom Stanley

                                          National Ecology Research Center
                                          Fort Collins, CO
From: tsa@cellar.org (The Silent Assassin)
Newsgroups: comp.ai.neural-nets
Subject: Need help on Neural-network learning
Date: 22 Jan 93 00:26:25 GMT
Organization: The Cellar BBS and public access system

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?

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

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
Newsgroups: comp.ai.neural-nets
From: IO10047@MAINE.MAINE.EDU
Newsgroups: comp.ai.neural-nets
Subject: WANTED: FEATURE EXTRACTION METHOD
Date: 26 Jan 93 17:47:24 GMT
Organization: University of Maine System

Orono
Jan 26 1993


Dear Sir/Madam,

	I am a graduate student in the Master's program at the University of

Maine in Orono.

	My thesis mainly focuses on recognition of handwritten numbers using

Neural Networks. The numbers that we are trying to use are "United States

Postal Service Office of Advanced Technology Handwritten ZIP Code Database

(1987)" supplied by the US Postal Service in Washington DC. This database

contains images of handwritten ZIP Codes.

	We are trying to use a set of features for the identification task.
	
We would be grateful if you could supply us with a database of features that

you might have used in your research.

	Even if you don't have the database available, any software that can

provide us with the following functions would be highly appreciated:

		- Binarization

		- Normalization

		- Separation

		- Feature Extraction

		- Identification using Neural Networks


	You can call me at: (207) 581-2025 / (207) 866-3606    or

	     	  email at: sharma@watson.eece.maine.edu


	Thanking You.


	Sincerely

	Lalit Sharma
From: naik@atlantis.rutgers.edu (Devang Naik)
Newsgroups: comp.ai.neural-nets
Subject: Character recognition using handwriting dynamics
Keywords: character, dynamics, pen based, horizontal/vertical motion
Date: 26 Jan 93 22:12:23 GMT
Organization: Rutgers Univ., New Brunswick, N.J.


	Hi netters,

	Last year in the NEC symposium, there was a talk on
	character recognition using handwriting via a pen based
	device, which is used to calculate the dynamics of the
	motion of the pen. Features unique to different characters
	were then extracted based on this dynamics. Multi-layer
	peceptrons were trained to recognize the 
	characters. The talk was by a group from Stanford.

	I would appreciate directions to any published material
	on this concept. Thanks in advance,

	Devang Naik,
	CAIP center,
	Rutgers University.
Newsgroups: comp.ai.neural-nets
From: jmyers@casbah.acns.nwu.edu (Jennifer Myers)
Subject: Re: Data for Travelling Salesman Prob Wanted...
Organization: Northwestern University, Evanston, Illinois
Date: Thu, 21 Jan 1993 18:07:02 GMT

sarit@jupiter.cse.utoledo.edu (sarit sharma) writes:

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

Sarit,

The 30-city TSP in Hopfield & Tank's 1985 paper in Biological Cybernetics
(Vol 52, pp. 141-152) uses approximately these coordinates for the cities:

 1.  (0.3663, 0.9076)	11.  (0.7459, 0.8713)	21.  (0.4521, 0.8465)
 2.  (0.7624, 0.7459)	12.  (0.7096, 0.7228)	22.  (0.0710, 0.7426)
 3.  (0.4224, 0.7129)	13.  (0.5908, 0.6931)	23.  (0.3201, 0.6403)
 4.  (0.5974, 0.6436)	14.  (0.3630, 0.5908)	24.  (0.6700, 0.5908)
 5.  (0.6172, 0.5495)	15.  (0.6667, 0.5446)	25.  (0.1980, 0.4686)
 6.  (0.3498, 0.4488)	16.  (0.2673, 0.4274)	26.  (0.9439, 0.4208)
 7.  (0.8218, 0.3795)	17.  (0.3729, 0.2690)	27.  (0.6073, 0.2640)
 8.  (0.4158, 0.2475)	18.  (0.5990, 0.2261)	28.  (0.3927, 0.1947)
 9.  (0.5347, 0.1898)	19.  (0.3960, 0.1320)	29.  (0.6287, 0.0842)
10.  (0.5000, 0.0396)	20.  (0.9802, 0.0182)	30.  (0.6832, 0.8515)

I measured these coordinates by hand after magnifying the image by 
several hundred percent with a photocopier.

Good luck,

Jennifer Myers
jmyers@nwu.edu
From: bnns93@cs.bham.ac.uk (British Neural Network Society)
Newsgroups: comp.ai.neural-nets,uk.events
Subject: BNNS'93
Date: 26 Jan 93 15:58:02 GMT
Distribution: uk
Organization: School of Computer Science, University of Birmingham, UK

BNNS'93 Symposium

Recent Advances in Neural Networks

Friday 29th January 1993, Lucas House, University of Birmingham


SOME PLACES STILL AVAILABLE


Email bnns93@uk.ac.bham.cs to register, payment on the door.
A few places will be available for registration on the day.

Late registration (after 27th. Jan) will incurr 5 pounds additional
administration charge.

Standard fee 55 pounds (30 student).

For directions/map, tel. 021 414 4773
Newsgroups: comp.ai.neural-nets
Subject: a beginner's quiestion
From: <K77BC@CUNYVM.BITNET>
Date: Tuesday, 26 Jan 1993 18:03:45 EST
Organization: City University of New York/ University Computer Center

  In the october's 1990 issue Mr. Larry O'brien published his
 C++ code for backprop NN.
 The was designed to work only with one hidden layer, but it was
 designed with multilayer upgrade in mind.
 I rewrote one function and NN now works ( i think ) with multiple
 hidden layers.

 My question is: When I run the XOR problem with one hidden layer
 it converges (abeit slowly). But if I use more than one hidden
 layer, it gets stucked (in local maximum?).
 Is this supposed to happen or It's just my code...?

Thank you.
                -Yevgeny  k77bc@cunyvm.cuny.edu

Newsgroups: comp.ai.neural-nets
From: aa4@doc.ic.ac.uk (A Agrawal)
Newsgroups: comp.ai.neural-nets
Subject: Neural Network for Speech Recogniton
Date: 26 Jan 1993 13:19:45 -0000
Organization: Department of Computing, Imperial College, University of London, UK.
Distribution: world
NNTP-Posting-Host: oak3.doc.ic.ac.uk


Hi,

   I want to do a project on recognising isolated words using
   a neural network.

   I would be grateful if someone could point in the right
   directon(book,papers,etc ..).

   Thanks in advance.
-- 
+-----------------------------------------------------------------------------+
| Ashish Agrawal                       A person who always speaks from        |
| aa4@doc.ic.ac.uk                     the heart will never have it broken.   |
+-----------------------------------------------------------------------------+
Newsgroups: comp.ai.neural-nets
From: KARGL@rzmain.rz.uni-ulm.de (ComRam)
Subject: Infos on Neural-Hardware
Organization: Chaos Computer Club Ulm
Date: Thu, 28 Jan 1993 16:48:15 GMT
X-News-Reader: VMS NEWS 1.25beta

For a seminary I am searching informations on the current state of hardware
developement for neural architectures. I am interested in any short reviews
about Hardware oriented Neural Projects like "Computer Guided Cars" etc.
Short texts or small Postscript files with little drawings would be more
than enough.

Thanx in advance ... Frank


-------------------------------------------------------------------------------
 ComRam  (in real life : Frank Kargl)   Student Consultant for VMS-Programming
 Internet      : COMRAM@RZ.UNI-ULM.DE   University of Ulm          Germany
 DECnet(Belwue): 50186::KARGL           All statements posted above are
 Bitnet        : KARGL@DULRUU51.BITNET  my personal opinion ONLY !
-------------------------------------------------------------------------------
  Use the SOURCE, Luke!             I feel a great disturbance in the SOURCE.
Newsgroups: comp.ai.neural-nets
From: wgliou@iastate.edu (Weih-Guang W Liou)
Subject: Re: Request: Refs on NNs for medical diagnosis
Organization: Iowa State University, Ames IA
Distribution: comp.ai.neural-nets
Date: Mon, 25 Jan 1993 20:29:25 GMT

Hi, 

  Try "D.Lowe and A.R. Webb, 'exploiting prior knowledge in network
       optimization : an illustration from medical prognosis' ,Network,
       vol. 1, no. 3, pp. 229-323, 1990 " .

  Best Regards,

  Liou at Ames.
From: Shiv Kaul
Newsgroups: comp.ai.neural-nets,bionet.neuroscience
Subject: SNNS simulator
Summary: xgui file is missing
Keywords: Neural nets, simulator
Date: 21 Jan 93 16:06:58 GMT
Organization: Bloomsbury Computing Consortium
Organisation: University College London

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.

The manual says it should be in the relevant directory, in this case,
xgui/bin/RS6000, but its empty.  

Any help is gratefully appreciated.  Please mail directly.

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   :
From: brooke@cco.caltech.edu (Brooke Paul Anderson)
Newsgroups: comp.ai.neural-nets
Subject: Re: Olfactory Cortex Simulation
Date: 22 Jan 1993 22:29:17 GMT
Organization: California Institute of Technology, Pasadena
NNTP-Posting-Host: sandman.caltech.edu

koehler@secs.ucsc.edu writes:

>Hi - I'm interested to know why the *olfactory* cortex is being simulated.
>Is it because of some special "connectedness" or is there anything at all
>to do with olfaction -the sense of smell?  
>-Hoping for some aspect of olfaction,
>	Ryan

The piriform cortex (i.e., the olfactory cortex) has a structure that
is similar to those of abstract associative-memory models -- I think that
is the main reason.  It might also be easier to study in the laboratory --
but I'm no expert on that.  It does have to do with the sense of smell
in that the receptors in the nose send information to the olfactory bulb,
which in turn sends information to the piriform cortex.  The piriform
cortex thus probably has to do with recognizing or learning to classify
odors.
From: chen@kuri.ces.kyutech.ac.jp (Chen Ke)
Newsgroups: comp.ai.neural-nets
Subject: Wanted Help: Handing contextual information
Keywords: object recognition, neural nets
Date: 27 Jan 93 13:55:11 GMT
Distribution: comp
Organization: Kyushu Institute of Technology, Iizuka, JAPAN.

Hi, everybody,

At present, I am interested in solving computer vision problems using
contextual
information. Resently, I investegated the information about this topic and
only
one paper is found for my requiring. This paper appears in IEEE Trans. PAMI,
Vol.13,No.10, 1991, titled "context-based vision: recognizing objects using
information from both 2-d and 3-d imagery". In this work, authors intented to
make the use of contextual information a central issues. For this purpose,
a system was designed to identify and use context as an integral part of
recognition.
As a result, I want to know if there is any work similar to this one.
Furthermore, is there any work about dealing with contextual information
problem by employing neural networks(not limited in the field of computer
vision)? By the way, I wish to know if there is any 3-d object recognition
system developed by means of neural networks techniques.

regards,

Ke Chen  < chen@kuri.ces.kyutech.ac.jp >
Prof. M. Ishikawa Research Group
Dept. of Control Engineering and Science
Kyushu Institute of Technology
Iizuka, Fukuoka 820, Japan
Tel: +81-948-29-7738
Fax: +81-948-29-7709
From: xm8@sdcc12.ucsd.edu (patrick k. simpson)
Newsgroups: comp.ai.neural-nets
Subject: NSF Message From Paul Werbos
Keywords: NSF, Program Focus, Neural Networks
Date: 27 Jan 93 17:01:56 GMT


[NOTE:  I am forwarding this for Paul Werbos, Program Manager at
 NSF for Neural Networks.  Please send any questions to Paul
 at "pwerbos@nsf.gov"  or call him at the number found at the
 bottom of the message.  Thanks, PKS]

------- Start of Forwarded Message

The Neuroengineering Program at NSF will be broadening the scope of
what it funds a little this year.

In particular, there is circa 100K new money earmarked for "HPCC" activities.
In the past, neuroengineering has not supported hardware-related research.
However, this year it will certainly be able to support optical neural net
work, and probably be able to cofund VLSI work as well.  IN the latter case,
however, we will still probably require coreview and cofunding from the
MIPS Division in Computer Science (for which one should consult Mike Foster,
(202)357-7853). Also, there is circa 100K earmarked for work related
to manufacturing; how this "relation" will be verified is unclear as yet,
but use of test problems of interest to manufacturing (or even letters 
indicating potential interest from manufacturers) would certainly help
increase the probability of funding. 

Beyond this, the basic funding levels and priorities are essentially
unchanged, as described in the Handbook of Intelligent Control
(D.White and D.Sofge, eds) -- neurocontrol, neural nets for
prediction or system identification, and improvements in
supervised learning. 

It would certainly be legal to copy this email over to other BBS's; in fact,
I hope someone does that kind of thing, to get the word out to the entire
community. 

If optically-related proposals do come in, of course, I would hope that they
would also make some contribution to the existing program priorities, and/or
involve the kinds of work which are too fundamental (e.g. Bell's Theorem 
effects or chaotic solitons) to be funded by the existing programs elsewhere. 

As in the past, the deadline for proposals is formally the end
of February, but I will try (without promising anything) to
schedule later proposals for panel review as well. 

Best wishes.

       Paul J. Werbos
       (202)-357-9618

------- End of Forwarded Message


Newsgroups: comp.ai.neural-nets
From: gperkins@netcom.com (Glen C. Perkins)
Subject: Re: Understanding NNs book - Caudill & Butler
Keywords: Neural nets
Organization: Netcom - Online Communication Services  (408 241-9760 guest) 
Date: Wed, 27 Jan 1993 23:33:04 GMT

I'm about halfway thru the first volume and I'd have to
say that if I didn't already understand the network
architectures, I don't think I'd be able to follow their
explanations in this book. I second Richard Helps'
recommendation that some other book be used to teach the
theory.
-- 
+-----------------------+---------------------------------------------------+
|--- Glen C. Perkins ---|                                                   |
| <gperkins@netcom.com> |             "Don't forget, your mind              |
| Native Guide Software |              only *simulates* logic."             |
| Palo Alto, California |                                                   |
+-----------------------+---------------------------------------------------+

Lines: 0
Date: Fri, 22 Jan 93 08:26:52 MST
Newsgroups: comp.ai.neural-nets,bionet.neuroscience
Subject: Re: SNNS simulator
Distribution: world
Organization: Brigham Young University, Provo UT USA
Keywords: Neural nets, simulator
From: rhelps@bones.et.byu.edu (Richard Helps)
From: xlfan@cesl.rutgers.edu (xlfan)
Newsgroups: comp.ai.neural-nets
Subject: algo. for data compression
Date: 28 Jan 93 04:53:43 GMT
Organization: Rutgers Univ., New Brunswick, N.J.

Hi, neural-neters, I am a new comer in this area, would you give me
some suggestions on the algorithms that are suitable for the purpose
of data compression? thank you for your time!

Since this may not be a common question, so please reply to: 
xlfan@cesl.rutgers edu
Newsgroups: comp.ai.neural-nets
From: uh311ae@sunmanager.LRZ-Muenchen.DE (Henrik Klagges)
Subject: Re: NetTalk
Organization: Leibniz-Rechenzentrum, Muenchen (Germany)
Date: Fri, 22 Jan 1993 17:46:56 GMT

falex@coue.loria.fr (Frederic Alexandre) writes:

>Do you know if it is possible to obtain such a corpus?

Get am6.tar.Z - the Version 6 of Aspirin/Migraines. They have done
a great job doing & explaining many important examples, including full
20k word nettalk.

Cheers, henrik@robots.ox.ac.uk
From: miguel@lilly.gris.informatik.uni-tuebingen.de (Miguel Encarnacao)
Subject: Wanted: Comments on MUME(5.0)
Keywords: mume
Organization: Comp. Center (ZDV) U of Tuebingen, FRG
Date: Mon, 25 Jan 1993 16:13:37 GMT

Does anybody have comments on MUME (V. 5.0) from the Sydney University?
Is it worth while to deal with?

Thanks, Miguel.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| Miguel Encarnacao                     | Universitaet Tuebingen, WSI/GRIS  |
| Office: [+49/0] (7071) 29-5462        | Auf der Morgenstelle 10, C9       |
| Fax:    [+49/0] (7071) 29-5466        | W-7400 Tuebingen, Germany         |
|---------------------------------------------------------------------------|
| email:  miguel@gris.informatik.uni-tuebingen.de                           |
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
--
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| Miguel Encarnacao                     | Universitaet Tuebingen, WSI/GRIS  |
| Office: [+49/0] (7071) 29-5462        | Auf der Morgenstelle 10, C9       |
| Fax:    [+49/0] (7071) 29-5466        | W-7400 Tuebingen, Germany         |
Newsgroups: comp.ai.neural-nets
From: adami@ghost.dsi.unimi.it (matteo adami)
Subject: new Bellcore Neural Chip - Info needed
Keywords: Neural Net, Chip, Speed, Noise
Organization: Computer Science Dep. - Milan University
Date: Thu, 28 Jan 1993 13:32:40 GMT
Summary: Information needed about how to generate thermal noise on chip.

I am with the University of Milano - ITALY. I am working at a Boltzmann Machine simulator based on a systolic array of processors. Our main problem is, of course, noise generation inside the PEs.

I am currently using a noise generator in Host, and transfer continuosly into the machine, loosing lot of computational power both in the Host machine and in the systolic one.
I know of a novel Neural Chip developed at Bellcore, which simulates Boltzmann Machines using a chip level solution to generate noise.
I could not find in our department library anything about this, in particular I didn't found anything about GOMAC conference in which I know Alspector & Jayakumar presented research results.

If anyone can post me any information about this chip, or further bybliographical information on these authors, i wuold appreciate.

If anyone is also working at Boltzmann Machines, I would very much like to exchange informations and share results. 


Adami Matteo - University of Milano
Computer Science Dept. - ITALY
e-mail : adami@ghost.sm.dsi.unimi.it 



Newsgroups: comp.ai.neural-nets
From: uh311ae@sunmanager.LRZ-Muenchen.DE (Henrik Klagges)
Subject: Re: Data for Travelling Salesman Prob Wanted...
Organization: Leibniz-Rechenzentrum, Muenchen (Germany)
Date: Fri, 22 Jan 1993 17:49:25 GMT

kadie@herodotus.cs.uiuc.edu (Carl M. Kadie) writes:
>Here is some background information on state-of-the-art traveling
>salesman problem solvers:

...

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.

Cheers, Henrik

Newsgroups: comp.ai.neural-nets
From: asta@thermal4.den.mmc.com (Brent Cullimore)
Subject: visualization
Organization: Martin Marietta Astronautics, Denver
Date: Mon, 25 Jan 1993 19:19:34 GMT

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.
Xref: sparky comp.ai.neural-nets:4999 news.answers:5485