HDTenEightyP/NetNews huggingface dataset:
From: sef@sef-pmax.slisp.cs.cmu.edu
Newsgroups: comp.ai.neural-nets
Subject: Re: CasCor2
Date: 28 Dec 92 21:10:50 GMT
Article-I.D.: cs.BzzMu3.F49.1
Organization: School of Computer Science, Carnegie Mellon


    From: simon@socrates.et.tudelft.nl (Natalio Simon)

    Is this new version (CasCor2) of the Cascade-Correlation Algorithm of Scott
    Fahlman available? 
    
    Maybe it is posible for Scott Fahlman to point out how CasCor2 differs from 
    CasCor1.
    
I've had several inquiries about "CasCor 2" since it was mentioned in Scott
Markel's workshop summary, so perhaps it is best to post the information
here.

The new algorithm is called "Cascade 2", not "Cascade Correlation 2" or
"Cascor 2" or "<anything> II".  (I hate Roman numerals.)  The reason for
the name change is that there's no "correlation" part left.  We have known
for some time that the correlation (really a covariance) measure used in
Cascor tends to produce hidden units that overshoot small errors.  This
isn't a problem for tasks with binary outputs, but leads to excess units
and poor generalization on tasks that require continuous-valued analog
outputs.  Cascade 2 uses a different algorithm for training the candidate
units, basically minimizing the difference between the candidate unit's
output and the residual error.  But there are a few tricky details I don't
want to explain right now.

I plan to release this C2 code generally as soon as I finish a few more
tests and write up a tech report on it.  I've been too busy scrounging for
funding for most of the past year to finish that task, but I hope to return
to it in the near future.  As soon as the TR and the code are generally
available, I'll announce that fact in this newsgroup.

-- Scott

===========================================================================
Scott E. Fahlman			Internet:  sef+@cs.cmu.edu
Senior Research Scientist		Phone:     412 268-2575
School of Computer Science              Fax:       412 681-5739
Carnegie Mellon University		Latitude:  40:26:33 N
5000 Forbes Avenue			Longitude: 79:56:48 W
Pittsburgh, PA 15213
===========================================================================
Newsgroups: comp.ai.neural-nets
From: pikoehn@cip.informatik.uni-erlangen.de (Philipp Koehn)
Newsgroups: comp.ai.neural-nets
Subject: Who knows US univ. that offer NN classes (graduate)?
Date: Thu, 31 Dec 1992 11:56:29 GMT
Organization: CSD., University of Erlangen
NNTP-Posting-Host: faui00d.informatik.uni-erlangen.de


Hi to all who have knowledge
                   (and who hasn't?),

I am planning to study one year at a graduate school at an American
university in 1993/94. Although, I already have some universities in
mind, I'm still searching for the ultima universitas.

Therefore, my question to the unknown reader:
Do you know an university, which offers graduate classes regarding
   * neural nets ,
   * connectionism ,
   * & such stuff ?
Then please mail me, perhaps even include broader information about
this institution.
 
Greetings from a far distant country  
                             (but we are hours earlier in 1993!)

Philipp K"ohn (or Koehn)  
                   ^^ ugly



---
 @@@                    Philipp K"ohn - FAU Erlangen (Germany)
@   @ 
  @ @                NIE WIEDER FASCHISMUS ! - NIE WIEDER KRIEG !
 @@@                 
  @                                 peace, man!
From: yoshio@eniac.seas.upenn.edu (Yoshio Yamamoto)
Newsgroups: comp.ai.neural-nets
Subject: CS Tech Report at CMU
Date: 31 Dec 92 21:16:23 GMT
Organization: University of Pennsylvania

Hi folks,

Sorry to waste the bandwidth with an irrelevant question.

Since I am a frequent reader of the newsgroup and know that this will be
seen by many people from CMU, I wonder if someone may be able to help me out.

One of my friends is desperate in getting the copy of a Computer Science 
Technical Report of CMU which was written by Wallace et. al., published in 
1986.  He knows neither the entire authors' names nor the exact title. 
Actually the paper was referred to by one of the reviewers for his submitted
journal paper on robotics, therefore he definitely needs to get it for the 
revision.

Any information will be greatly appreciated.


Yoshio Yamamoto
General Robotics and Active Sensory Perception Laboratory
University of Pennsylvania
Newsgroups: comp.ai.neural-nets
From: t3611ad@sun7.LRZ-Muenchen.DE (Xinzhi Li)
Subject: Re: Kohonen nets with other than rectangular output arrays
Organization: Leibniz-Rechenzentrum, Muenchen (Germany)
Distribution: lrz
Date: Tue, 22 Dec 1992 10:34:33 GMT

To the question:

>What about other neighbourhood graphs?
 e.g. toroidal, trees etc.

If you just want a quanzation of you data set, the neighborhood
graph (network topology) is, by my experience, rather irrelevant.  If
you want to preserve the topological information of the data set in the
network, the toroidal network topology is more favorable than the
rectangular topology, since it is boundaryless, all its neurons are
treated equally during the training process and hence more homogeneous.

The following paper presented a type of Kohonen net with adaptive tree
topology which seems to be very flexible and general. It is, however,
much more difficult to implement than the rectangluar or toroidal
network.

J.A. Kangas, T.K. Kohonen and J.T. Laaksonen: Variant of
Self-Organizing Maps. IEEE Trans. on Neural Networks, vol.1. No.1 March
1990. pp. 93-99.

enar.eros.chemie.tu-muenchen.de
X. Li
Newsgroups: comp.ai.neural-nets
Subject: Green's Function and Neural Network Learning
From: rangan@cs.tulane.edu (Sudha Rangan)
Date: 25 Dec 92 05:03:22 PT

Nntp-Posting-Host-[nntpd-3754]: hermes
Organization: Computer Science Dept., Tulane Univ., New Orleans, LA
Date: Wed, 23 Dec 1992 17:13:08 GMT



I have a question in regularization theory, which provides
a framework for interpolation, in neural network learning. 

The function that minimizes the error function 
is a linear combination of several Green's functions. 
The coefficients of this linear combination satisfy a system
of linear equations, where the coefficient matrix depends on
evaluations of Green's functions at specific points. 

The problem is Green's function can be singular in certain
circumstances. Does it mean regularization approach works
only for the case when Green's functions are non-singular,
or there are other approaches?

Thanks in advance and Merry X'mas and Happy New Year!

Xref: sparky comp.ai.neural-nets:4630 comp.speech:376
From: indridi@rhi.hi.is (Indridi Bjornsson)
Newsgroups: comp.ai.neural-nets,comp.speech
Subject: Graduate study in NN
Date: 21 Dec 92 09:41:05 GMT
Followup-To: poster




      Hi :-)


   I'm currently planning a graduate study.  My main interests are 
Neural Networks and Speech Recognition. I'm looking for good schools 
that offer graduate programs and are active in either (or better yet) 
both areas.

   Any information will be appreciated,
thanks in advance,
Indridi.


----------------------------------------------------------------------------
Indridi Bjornsson     | All parts of this publication may be reproduced, in |
Taeknigardur,Dunhaga 5| any form or by any means, except intensive sexual   |
107 Reykjavik         | harassment against elderly animals and children,    |
ICELAND               | without prior written permission of the publisher.  |
----------------------------------------------------------------------------

-- 
----------------------------------------------------------------------------
Indridi Bjornsson     | All parts of this publication may be reproduced, in |
Taeknigardur,Dunhaga 5| any form or by any means, except intensive sexual   |
107 Reykjavik         | harassment against elderly animals and children,    |
Newsgroups: comp.ai.neural-nets
From: cchiu@top.cis.syr.edu (Ching-Tai Chiu)
Subject: Restriction on the range of weights
Organization: Syracuse University, CIS Dept.
Date: Thu, 31 Dec 92 16:08:40 EST

Hi,
Is there any BP learning rule that can restrict the weights
between some given range? Thanks.

C. T. Chiu
cchiu@top.cis.syr.edu
CIS, Syracuse University
From: rangan@cs.tulane.edu (Sudha Rangan)
Newsgroups: comp.ai.neural-nets
Subject: Green's Function and Neural Network Learning
Date: 23 Dec 92 17:13:08 GMT
Organization: Computer Science Dept., Tulane Univ., New Orleans, LA
Nntp-Posting-Host-[nntpd-3754]: hermes



I have a question in regularization theory, which provides
a framework for interpolation, in neural network learning. 

The function that minimizes the error function 
is a linear combination of several Green's functions. 
The coefficients of this linear combination satisfy a system
of linear equations, where the coefficient matrix depends on
evaluations of Green's functions at specific points. 

The problem is Green's function can be singular in certain
circumstances. Does it mean regularization approach works
only for the case when Green's functions are non-singular,
or there are other approaches?

Thanks in advance and Merry X'mas and Happy New Year!
Newsgroups: comp.ai.neural-nets
From: dr788307@cs.nthu.edu.tw (dr78)
Subject: Neural Reinforcement Learning?
Organization: National Tsing Hua University (HsinChu)
Date: Mon, 28 Dec 1992 14:28:32 GMT

  Sutton's Adaptive Heuristic Critic (AHC) and Watkin's Q-learning are
two popular reinforcement learning mechanisms, and their original designs
are based on storing utilities on a look-up table structure. In order to 
properly predict (interpolate) utilities of unvisited states and to 
compress the utility storage, many literature tried and reported progresses 
in neural implementation. 

  However, this will introduce new problems: (1) updating the utility of a state
may undesiredly cause a large change the utilities of other states. Besides,
(2) the training set seems to be more or less conflict since we have many utilities
needed to assign to a state in the updating process. 

  If we just use the on-line backpropagation to train a MLP (feeding a pair
of state and its new utility), the problem (1) and (2) will occur and (2) 
emphasizes the problem (1).  If we use a batch backpropagation, the table storage
will be necessary to keep a single utility of each state (to avoid the problem (2)).
Obviously, this kind of neural implementation is guided by the symbolic knowledge
and violates the original purpose of saving memory. 

  To date, the reported results seem to be limited on small applications. Thus,
the batch backpropagation seems to be the way which those researchers chose. Am
I right? But, this seems to a bad neural implementation since the training set
(stored in a table) is guided by symbolic knowledges. 

  Any comments will be highly appreciated!!

-------------------------------------------------------------------------------
Hown-Wen Chen
e-mail: dr788307@cs.nthu.edu.tw
Newsgroups: comp.ai.neural-nets
From: skubi@prl.dec.com (Marcin Skubiszewski)
Subject: Announcement: A hardware implementation of the Boltzmann machine
Organization: Digital Equipment Corporation - Paris Research Laboratory
Date: Tue, 22 Dec 1992 14:39:29 GMT

The subject line says it all. For details, get a copy of the article "An Exact
Hardware Implementation of the Boltzmann Machine". The article appeared in the
Fourth IEEE Symposium on Parallel Distributed Processing. To get a postscript
copy of it, send mail containing the following lines in the header:

To: doc-server@prl.dec.com
Subject: send SKU3.ps


				Abstract

We present a faithful hardware implementation of the Boltzmann machine. The
prototype performs 505 megasynapses (million of additions and multiplications)
per second, using 16-bit fixed-point weights. It can emulate fully connected
instances of the Boltzmann machine containing up to 1438 variables.
Xref: sparky comp.ai.neural-nets:4652 news.answers:4749
From: finin@cs.umbc.edu (Timothy Finin)
Newsgroups: comp.ai.neural-nets
Subject: 9th IEEE Conf. on AI for Applications (CAIA-93) program
Date: 3 Jan 1993 12:48:42 -0500
Organization: Computer Science, University of Maryland Baltimore County
Distribution: world
NNTP-Posting-Host: algol.cs.umbc.edu
Summary: send email to caia@cs.umbc.edu to get program
Keywords: CAIA, Applied AI, Conference

The preliminary advance program and registration information for CAIA-93
(the 9th IEEE Conference on Artificial Intelligence for Applications) is
now available.  The conference will be held March 1-5, 1993 at the Disney
Yacht & Beach Club Resort Hotel in Orlando, Florida, USA.  To receive an
automatic response with the advance program and registration information
by email, send an email message to CAIA@CS.UMBC.EDU.

For eight consecutive years, the IEEE Computer Society's Conference on
Artificial Intelligence for Applications has provided an international
forum for the exchange of ideas, techniques, and experiences in the
application of AI to real-world problems. During this time, CAIA has
established itself as a key conference for business and technical people
who want to keep up on the fast-changing word of commercial AI.  This
year, the ninth annual CAIA will continue that tradition by providing a
provocative mix of introductory, intermediate and advanced talks,
tutorials, workshops, and panels.  The plenary talks will be given by
Patrick H. Winston (MIT) on Database Mining, Wendy Lehnert (UMASS) on
"Portability and Scalability for Information Extraction Systems" and Frank
Mayadas (Alfred T. Sloan Foundation) on "Computer Technology Outlook and
Applications"

CAIA-93 will be held at the Disney Yacht & Beach Club Resort Hotel, a
world class hotel on the Disney property.  Due to some special
circumstances, we have obtained exceptionally good rates.

For registration or general information, contact: CAIA '93 Registration,
IEEE Computer Society, 1730 Mass. Ave., N.W., Washington, DC 20036-1992
Voice: 202-371-1013; Fax: 202-728-0884, email: j.harward@compmail.com.




Newsgroups: comp.ai.neural-nets
From: jmains@engr.latech.edu (John P. Mains)
Subject: Neural Net PROBLEM for you Experts
Reply-To: jmains@engr.latech.edu
Organization: Louisiana Tech University
Distribution: usa
Date: Mon, 4 Jan 1993 02:50:10 GMT

I understand only the basics of neural nets and unfortunately don't have the 
time to do an in depth study of what I need.  Therefore I give you this problem (please email me)

I have 50-60 inputs
I need a neural net capable of recognizing about 40,000 to 50,000 patterns.
I would prefer real time processing to accuracy, or as close as possible.

What I need to know is the kind of net I need, and as much specifics about it you can give me.
If you know of a better way to do this other than nets, input would be appreciated.
-------------
Thanx
JP
jmains@engr.latech.edu

Newsgroups: comp.ai.neural-nets
From: yuwaraj@ecf.toronto.edu (YUWARAJ Murugathas)
Subject: EMG data wanted ...
Keywords: Myoelectric signals.
Organization: University of Toronto, Engineering Computing Facility
Date: Sun, 27 Dec 1992 08:33:46 GMT

Hello everyone,

        Happy New Year .....

I am doing my thesis on classification of Myoelectric signals using Artificial
Neural Networks.
I would very much appreciate if some one could kindly point me to a source of
sampled EMG
signals (preferably in the internet).
Technical details are as follows:
Muscle sites : Those muscles that are responsible for wrist functions -
pronation, supination, 
extension and flexion.
Format : Preferably ASCII 

I hope to use these data for my preliminary tests and eventually use my own data.
Kindly respond by email:     yuwaraj@ecf.toronto.edu
Thank you.

Murugathas (Thas) Yuwaraj
Division of Engineering Science (Elec. Option)
University of Toronto.
                       Land of the "Blue Jays"
From: hoffmann@sri.com (Marcelo Hoffmann)
Newsgroups: comp.ai.neural-nets
Subject: NNs to help disabled?
Date: 22 Dec 92 18:05:38 GMT
Followup-To: comp.ai.neural-nets
Organization: SRI International

Hello, 
I am trying to find out about research on the use of neural networks to
classify brainwaves of speech and movement-impaired people to allow them to
communicate.  I understand researchers at Flinders University, Adelaide,
Australia have done some related work...  Any leads or contacts out there?
I will be happy to summarize any messages sent directly to me.  

Thanks. Marcelo 

Marcelo Hoffmann
SRI International
333 Ravenswood Ave.
Menlo Park, CA 94025
hoffmann@sri.com
tel 415.859.3680
Newsgroups: comp.ai.neural-nets
From: andersen@sgi.elec.uq.oz.au (Hans Andersen)
Subject: Neural Control - Looking for someone (in Melbourne)
Summary: Looking for someone whose E-mail address I lost.
Keywords: neural control
Organization: Prentice Centre, University of Queensland
Date: Sun, 27 Dec 1992 02:35:08 GMT

A while ago I sent someone a report on Neural Control after a request
in this news group.  I think he was at a university in Melbourne,
Australia.  If you think you are that person could you please send me
a message.  I just want to tell you something about the report.
Thanks.

Hans Andersen
<andersen@s1.elec.uq.oz.au>
Department of Computer and Electrical Engineering
University of Queensland
Australia.
From: slqqv@cc.usu.edu
Newsgroups: comp.ai.neural-nets
Subject: HWRecognition Project
Date: 1 Jan 93 23:58:47 MDT
Organization: Utah State University


I have heard there is /or was a handwriting recognition project going at Univ
at Buffalo by the name of CEDAR. I want to have some more information about
it and would like to know whether it used neural nets.

Thanks

Shekhar
 











      


F
From: Steve.Hayes@f20.n7101.z5.fidonet.org (Steve Hayes)
Newsgroups: comp.ai.neural-nets
Subject: Genealogy & neural nets
Date: Wed, 23 Dec 92 09:36:00 GMT
Organization: FidoNet node 5:7101/20 - Unisa Editorial, Pretoria

Does anyone know an e-mail address for Dr Peter Cooley of Aston University in 
Birmingham, UK?

He is apparently developing some genealogy software using the principles of 
neural networks.



--  
INTERNET: Steve.Hayes@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.
Newsgroups: comp.ai.neural-nets
From: sthomas@nimbus.worldbank.org (Sidney F. Thomas)
Subject: Togai Infralogic -- Address Requested
Organization: The World Bank, Washington, DC, USA
Date: Mon, 28 Dec 1992 00:44:03 GMT

Subject line says it all.  Grateful if some netter could give me
a street address and contact for Togai Infralogic.

--Sidney Thomas
From: peter@canne.cs.umu.se (Peter Lundberg)
Newsgroups: comp.ai.neural-nets
Subject: Genesis
Date: 28 Dec 92 17:01:00 GMT
Organization: Dep. of Info.Proc, Umea Univ., Sweden

Finally, after having waited about six month
the book "Handbook of Genetic Algorithms" by Lawrence Davis
reached me here in Sweden. Now I find that the GA-system
"Genesis" is only available on an optional diskette!

And I don't feel like waiting another year or so, so instead
I ask you netters, is Genesis available via ftp from somewhere?
If so, please give me a hint, I really would like to see this 
thing in motion.

peter@cs.umu.se

peter lundberg
University of Ume}
Sweden
From: ttj10@eng.cam.ac.uk (Tim Jervis)
Newsgroups: comp.ai.neural-nets
Subject: Technical report: real pole balancing
Date: 21 Dec 92 18:19:36 GMT
Organization: Engineering Department, Cambridge University, England.

The following technical report is available via the Cambridge
University ftp archive svr-ftp.eng.cam.ac.uk. Instructions for
retrieval from the archive follow the summary.

------------------------------------------------------------------------------
		 
		 Pole Balancing on a Real Rig using a
		  Reinforcement Learning Controller


		  Timothy Jervis and Frank Fallside


		 Cambridge University Engineering Department
			Cambridge CB2 1PZ, England



				Abstract

In 1983, Barto, Sutton and Anderson~\cite{Barto83} published details
of an adaptive controller which learnt to balance a simulated inverted
pendulum. This {\em reinforcement learning} controller balanced the
pendulum as a by-product of avoiding a cost signal delivered to the
controller when the pendulum fell over. This paper describes their
controller learning to balance a real inverted pendulum.  As far as
the authors are aware, this is the first example of a reinforcement
learning controller being applied to a real inverted pendulum learning
in real time.

The results show that the controller was able to improve its
performance as it learnt, and that the task is computationally
tractable. However, the implementation was not straightforward.
Although some of the controller's parameters were tuned automatically
by learning, some were not and had to be carefully set for successful
control. This limits the usefulness of this kind of learning
controller to small problems which are likely to be better controlled
by other means. Before a learning controller can tackle more difficult
problems, a more powerful learning scheme has to be found.

------------------------------------------------------------------------------

FTP INSTRUCTIONS:

unix> ftp svr-ftp.eng.cam.ac.uk
Name: anonymous
Password: (your_userid@your_site)
ftp> cd reports
ftp> binary
ftp> get jervis_tr115.ps.Z
ftp> quit
unix> uncompress jervis_tr115.ps.Z
unix> 

--
  _/_/_/_/_/  _/  _/      _/   Cambridge University
     _/	     _/  _/_/  _/_/    Engineering Department,
    _/ 	    _/  _/  _/  _/     Trumpington Street,
       	                       Cambridge CB2 1PZ, England
Newsgroups: comp.ai.neural-nets
From: simon@socrates.et.tudelft.nl (Natalio Simon)
Subject: CasCor2
Organization: Delft University of Technology, Dept. of Electrical Engineering
Date: Mon, 28 Dec 1992 11:59:20 GMT

Is this new version (CasCor2) of the Cascade-Correlation Algorithm of Scott
Fahlman available? 

Maybe it is posible for Scott Fahlman to point out how CasCor2 differs from 
CasCor1.

Thanks in advance.

Natalio Simon
Delft University of Technology
The Netherlands

Email simon@socrates.et.tudelft.nl
From: twallace@gmuvax2.gmu.edu (Todd Wallace)
Subject: Re: Looking for PC based expert systems.
Keywords: general, learning
Organization: George Mason University, Fairfax, Va.
Distribution: inet
Date: Wed, 30 Dec 1992 16:52:11 GMT

I am working on a model of a generalized learning paradigm to be
modelled in an object-oriented environment (C++).  Ideally, I
would like to end up with a module that you "plug in" to a
program, hooking up its inputs and outputs, set rewards and
goals, and off it would go, figuring out its environment as it
went.  

I am approaching this problem from the point of view of
biological system modelling, something like if you were to
design a Turing machine or Life game "cell" that is reinforced
for performing certain actions in its environment.  For
instance, you could theoretically find a Turing machine that
would sort letters introduced into the left edge of its
environment and transport them to the right edge of its
environment.  Each Turing machine could form a link in a whole
assembly-line type "factory" that would eventually make a
working program.

I don't want to reinvent the wheel, though, so I'd like to start
out by compiling a bibliography of related research.  However, I
am handicapped because I don't know what this line of AI
research would be called.  Does anyone out there in AI land
know?
Thanks for your help.
---------------------
Todd Wallace
twallace@gmuvax2.gmu.edu



Xref: sparky comp.ai:4684 comp.ai.philosophy:7139