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Neuron Digest Volume 12 Number 01
Neuron Digest Wednesday, 8 Sep 1993 Volume 12 : Issue 1
Today's Topics:
Administrivia, New Volume
Kohonen maps & LVQ -- huge bibliography (and reference request)
Please post this announcement of a new book
PC Based NN Software
Using artif. neural nets in QSAR's
nonlinear controllers
SNNS-info
Re: Inquiries
Cognitive scientist position at Cornell
AM6 Users: release notes and bug fixes available
Send submissions, questions, address maintenance, and requests for old
issues to "neuron-request@psych.upenn.edu". The ftp archives are
available from psych.upenn.edu (130.91.68.31). Back issues requested by
mail will eventually be sent, but may take a while.
----------------------------------------------------------------------
Subject: Administrivia, New Volume
From: "Neuron-Digest Moderator, Peter Marvit" <neuron@psych.upenn.edu>
Date: Wed, 08 Sep 93 18:00:11 -0500
Dear readers,
As you will notice, and as has been traditional at the start of the
academic year, we begin a new volume with this issue -- V12. The
readership has continued to grow and the master list has nearly 1800
addresses from all over the world and all types of people. Many of the
address are local redistribution aliases and one is gatewayed to the
USENET news group comp.ai.neural-networks, so the actual readership of
the Digest is quite large.
I want to thank you all, once again, for your continued support and
suggestions. Without your efforts and contributions, the Digest would
not work.
This fall, I hope to put the back issues both on a gopher server and a
WAIS server. In addition, I hope to get a mail server up and running so
people without Internet access can get at the archives. I will make an
announcement when/if these happen. I certainly know we need a table of
contents for the archives and that will be a first priority.
The diveristy of topics discussed in this forum reflects the diversity of
our readers. I hope it stays that way...
-Peter
: Peter Marvit, Neuron Digest Moderator <neuron-request@psych.upenn.edu> :
: Courtesy of the Psychology Department, University of Pennsylvania :
: 3815 Walnut St., Philadelphia, PA 19104 w:215/898-6274 h:215/387-6433 :
------------------------------
Subject: Kohonen maps & LVQ -- huge bibliography (and reference request)
From: Bibliography <biblio@nucleus.hut.fi>
Date: Tue, 31 Aug 93 13:08:00 +0700
Hello,
We are in the process of compiling the complete bibliography
of works on Kohonen Self-Organizing Map and Learning Vector
Quantization all over the world. Currently the bibliography
contains more than 1000 entries. The bibliography is now
available (in BibTeX and PostScript formats) by anonymous FTP from:
cochlea.hut.fi:/pub/ref/references.bib.Z ( BibTeX file)
cochlea.hut.fi:/pub/ref/references.ps.Z ( PostScript file)
The above files are compressed. Please make sure you use "binary" mode
when you transfer these files.
Please send any additions and corrections to :
biblio@cochlea.hut.fi
Please follow the IEEE instructions of references (full names of
authors, name of article, journal name, volume + number where applicable,
first and last page number, year, etc.) and BibTeX-format, if possible.
Yours,
Jari Kangas
Helsinki University of Technology
Laboratory of Computer and Information Science
Rakentajanaukio 2 C
SF-02150 Espoo,
FINLAND
------------------------------
Subject: Please post this announcement of a new book
From: valmir@vnet.IBM.COM
Date: Wed, 01 Sep 93 08:58:36 -0300
MASSIVELY PARALLEL MODELS OF COMPUTATION
Distributed Parallel Processing in Artificial Intelligence
and Optimization
Valmir C. Barbosa
Ellis Horwood Series in Artificial Intelligence
Ellis Horwood/Simon & Schuster, 1993
telephone: +44-442-881900
fax: +44-442-882099
ISBN 0-13-562968-3, approximate price: US$ 62.95
ABSTRACT
This book covers the simulation by distributed parallel computers of
massively parallel models of interest in artificial intelligence and
optimization, bringing together two major areas of current interest
within computer science --- distributed parallel processing and
massively parallel models in artificial intelligence and optimization.
Throughout ten chapters, a series of important massively parallel
models of computation are surveyed, including cellular automata,
Hopfield neural networks, Markov random fields, Bayesian networks,
and other more specialized neural networks with important applications
to the solution of mathematical problems. Emphasis is placed on the
dynamic behavior of these models, and on how some fundamental
techniques of distributed parallel program design can be employed
for their simulation by parallel computers. In addition, the main
application areas of each model are also discussed, as well as how
the models interrelate to one another.
The book has been intended to have a multidisciplinary character,
and will appeal to professionals and students in a variety of fields,
as in computer science, electrical engineering, and cognitive science.
CONTENTS
Preface
PART 1. Introduction and Background
Chapter 1. Introduction
Chapter 2. Background
PART 2. Fundamentals of Distributed Parallel Computation
Chapter 3. Models of Distributed Parallel Computation
Chapter 4. Timing and Synchronization
PART 3. Fully Concurrent Automaton Networks
Chapter 5. Cellular Automata
Chapter 6. Analog Hopfield Neural Networks
Chapter 7. Other Analog Neural Networks
PART 4. Partially Concurrent Automaton Networks
Chapter 8. Binary Hopfield Neural Networks
Chapter 9. Markov Random Fields
Chapter 10. Bayesian Networks
PART 5. Appendices
Appendix A. A Distributed Parallel Programming Primer
Appendix B. The Software for Automaton Network Simulation
Appendix C. Long Proofs
Appendix D. Additional Edge-Reversal Properties
Bibliography
Author Index
Subject Index
------------------------------
Subject: PC Based NN Software
From: bcrowder@mail.lmi.org
Date: Wed, 01 Sep 93 10:20:27 -0500
I have just started my quest to understand NN. I am looking at
several products such as Ward's NeuroShell. I would prefer to work in
a Visual Basic 3.0 framework to failitate links to data sources. Are
there any products that folks have found that they like in that
environment. Any comments on other environments on the PC would be
appreciated. I want to work with stock data and decision criteria for
a buy/sell point.
Thanks,
Bill Crowder //bcrowder@lmi.org//
------------------------------
Subject: Using artif. neural nets in QSAR's
From: Bernard Budde <BUDDE@SC.AGRO.NL>
Date: Wed, 01 Sep 93 17:07:00 +0000
Neuron Digesters,
In Neuron Digest 11(50), 31 Aug 1993, David Manallack wrote:
(part of the original message)
> Subject: Commercial Neural Network Software
> From: manallack_d%frgen.dnet@smithkline.com (NAME "David Manallack")
> Date: Fri, 27 Aug 93 09:39:54 -0500
>
>
> As you may be aware, networks have found various
> uses in chemistry (e.g Quantitative Structure-Activity Relationships
> (QSAR)), typically using back propagation algorithms.
>
> David Manallack email: manallack_d%frgen.dnet@smithkline.com
> David Livingstone email: livingstondj@smithkline.com
I am working in the QSAR-field and I spend a small amount of my time on neural
nets. For this purpose I use a home-brew back-prop net. So far I have seen only
very few articles on the subject, and *none* in which I find the conclusions
justified by the data.
Therefore, I am very interested in all information that deals with the use of
neural-nets in QSAR studies (titles, FTP-sites, code, data-sets... anything).
Please send your response to: budde@sc.agro.nl. I'll send a summary to Neuron
Digest in October.
>From below the sea-level, Bernard Budde budde@sc.agro.nl
------------------------------
Subject: nonlinear controllers
From: garcia@ece.nps.navy.mil (Randall Garcia 12-94)
Date: Wed, 01 Sep 93 14:52:55 -0800
I'm looking for information on using nnets for controlling a nonlinear
plant transfer function.
The problem, in particular, is a derivative of the classic "broomstick"
problem in which control is applied to ensure a pendulum type set up
remains vertical when placed on a moving cart.
My application is that of a pitch plane controller for a tail driven
missile. I would like to see if anyone has modelled this system and
controlled it using a nnet with backpropagation.
Please send info or examples to:
R. E. Garcia
NPS code EC
Monterey, California 93943
or email
garcia@ece.nps.navy.mil
TIA: REG
------------------------------
Subject: SNNS-info
From: mph295@bio-medical-physics.aberdeen.ac.uk ("j.carson")
Date: Thu, 02 Sep 93 12:06:45 +0000
Hello my name is James Carson. I am doing a Phd at Aberdeen
university medical physics department. My subject is neural networks
techniques applied to medical image processing/analysis. I have just
obtained using ftp a copy of SNNS version 3.0 and am in the processes of
learning how to use it. I would greatly appreciate any feedback from
others who may have used it already. especially the ART1 and ART2
features. Also i would like to hear from anyone who has attenpted to use
neural networks to enhance images. I seem to find that most of the
techniques work well with images containing lost of nicely defined edges
but not with medical images such as mammagrams. Has anyone else had much
success with images other than black tables on white backgrounds.
yours sincerely
James
mph295@uk.ac.abdn.biomed
------------------------------
Subject: Re: Inquiries
From: David Kanecki <kanecki@cs.uwp.edu>
Date: Fri, 03 Sep 93 14:07:14 -0600
Dear Peter,
On the 2d Neural Chess, 3d Neural Chess, and High Volume Data Analysis
Process Controllers, please contact me directly at kanecki@cs.uwp.edu.
On the 1992 and 1993 published papers on 2d and 3d Neural Chess respectively,
please contact the Society for Computer Simulation at (619)-277-3888.
Also, I have been appointed head of the Emergency Planning Committee for
the Society for Computer Simulation. If individuals would like to submit
papers on Emergency Planning for the April Multiconference please sent it
to:
SCS
1994 Simulation Multiconference/ Emergency Planning
P.O. Box 17900
San Diego, CA 92177
Thank you for your assistance.
Sincerely,
David H. Kanecki, A.C.S., Bio. Sci.
kanecki@cs.uwp.edu
P.O. Box 26944
Wauwatosa, WI 53226-0944
------------------------------
Subject: Cognitive scientist position at Cornell
From: tvs1@Cornell.edu (Tom Smulders)
Organization: Cornell University
Date: 08 Sep 93 18:06:43 +0000
COGNITIVE PSYCHOLOGIST, CORNELL UNIVERSITY
The Department of Psychology at Cornell University is considering
candidates for a tenure-track assistant professorship in any area of
cognition. Areas of specialization include but are not limited to:
memory, attention, language and speech processing, concepts, knowledge
representation, reasoning, problem solving, decision making, mathematical
psychology, motor control and action. The position will begin in August,
1994. Review of applications will begin December 1, 1993. Cornell
University is an Equal Opportunity/Affirmative Action Employer
Interested applicants should submit a curriculum vitae, reprints or
preprints of completed research, and letters of recommendation sent
directly from three referees to:
Secretary, Cognitive Psychology Search Committee
Department of Psychology, Uris Hall, Cornell University
Ithaca, NY 14853-7601, USA.
email: kas10@cornell.edu
FAX: 607-255-8433 Voice: 607-255-6364
------------------------------
Subject: AM6 Users: release notes and bug fixes available
From: Russell R Leighton <taylor@world.std.com>
Date: Sun, 29 Aug 93 22:21:27 -0500
There has been an update to the am6.notes file at the AM6
ftp sites. User's not on the AM6 users mailing list
should get this file and update their installation.
Russ
======== REPOST OF AM6 RELEASE (long) ========
The following describes a neural network simulation environment
made available free from the MITRE Corporation. The software
contains a neural network simulation code generator which generates
high performance ANSI C code implementations for modular backpropagation
neural networks. Also included is an interface to visualization tools.
FREE NEURAL NETWORK SIMULATOR
AVAILABLE
Aspirin/MIGRAINES
Version 6.0
The Mitre Corporation is making available free to the public a
neural network simulation environment called Aspirin/MIGRAINES.
The software consists of a code generator that builds neural network
simulations by reading a network description (written in a language
called "Aspirin") and generates an ANSI C simulation. An interface
(called "MIGRAINES") is provided to export data from the neural
network to visualization tools. The previous version (Version 5.0)
has over 600 registered installation sites world wide.
The system has been ported to a number of platforms:
Host platforms:
convex_c2 /* Convex C2 */
convex_c3 /* Convex C3 */
cray_xmp /* Cray XMP */
cray_ymp /* Cray YMP */
cray_c90 /* Cray C90 */
dga_88k /* Data General Aviion w/88XXX */
ds_r3k /* Dec Station w/r3000 */
ds_alpha /* Dec Station w/alpha */
hp_parisc /* HP w/parisc */
pc_iX86_sysvr4 /* IBM pc 386/486 Unix SysVR4 */
pc_iX86_sysvr3 /* IBM pc 386/486 Interactive Unix SysVR3 */
ibm_rs6k /* IBM w/rs6000 */
news_68k /* News w/68XXX */
news_r3k /* News w/r3000 */
next_68k /* NeXT w/68XXX */
sgi_r3k /* Silicon Graphics w/r3000 */
sgi_r4k /* Silicon Graphics w/r4000 */
sun_sparc /* Sun w/sparc */
sun_68k /* Sun w/68XXX */
Coprocessors:
mc_i860 /* Mercury w/i860 */
meiko_i860 /* Meiko w/i860 Computing Surface */
Included with the software are "config" files for these platforms.
Porting to other platforms may be done by choosing the "closest"
platform currently supported and adapting the config files.
New Features
- ------------
- ANSI C ( ANSI C compiler required! If you do not
have an ANSI C compiler, a free (and very good)
compiler called gcc is available by anonymous ftp
from prep.ai.mit.edu (18.71.0.38). )
Gcc is what was used to develop am6 on Suns.
- Autoregressive backprop has better stability
constraints (see examples: ringing and sequence),
very good for sequence recognition
- File reader supports "caching" so you can
use HUGE data files (larger than physical/virtual
memory).
- The "analyze" utility which aids the analysis
of hidden unit behavior (see examples: sonar and
characters)
- More examples
- More portable system configuration
for easy installation on systems
without a "config" file in distribution
Aspirin 6.0
- ------------
The software that we are releasing now is for creating,
and evaluating, feed-forward networks such as those used with the
backpropagation learning algorithm. The software is aimed both at
the expert programmer/neural network researcher who may wish to tailor
significant portions of the system to his/her precise needs, as well
as at casual users who will wish to use the system with an absolute
minimum of effort.
Aspirin was originally conceived as ``a way of dealing with MIGRAINES.''
Our goal was to create an underlying system that would exist behind
the graphics and provide the network modeling facilities.
The system had to be flexible enough to allow research, that is,
make it easy for a user to make frequent, possibly substantial, changes
to network designs and learning algorithms. At the same time it had to
be efficient enough to allow large ``real-world'' neural network systems
to be developed.
Aspirin uses a front-end parser and code generators to realize this goal.
A high level declarative language has been developed to describe a network.
This language was designed to make commonly used network constructs simple
to describe, but to allow any network to be described. The Aspirin file
defines the type of network, the size and topology of the network, and
descriptions of the network's input and output. This file may also include
information such as initial values of weights, names of user defined
functions.
The Aspirin language is based around the concept of a "black box".
A black box is a module that (optionally) receives input and
(necessarily) produces output. Black boxes are autonomous units
that are used to construct neural network systems. Black boxes
may be connected arbitrarily to create large possibly heterogeneous
network systems. As a simple example, pre or post-processing stages
of a neural network can be considered black boxes that do not learn.
The output of the Aspirin parser is sent to the appropriate code
generator that implements the desired neural network paradigm.
The goal of Aspirin is to provide a common extendible front-end language
and parser for different network paradigms. The publicly available software
will include a backpropagation code generator that supports several
variations of the backpropagation learning algorithm. For backpropagation
networks and their variations, Aspirin supports a wide variety of
capabilities:
1. feed-forward layered networks with arbitrary connections
2. ``skip level'' connections
3. one and two-dimensional weight tessellations
4. a few node transfer functions (as well as user defined)
5. connections to layers/inputs at arbitrary delays,
also "Waibel style" time-delay neural networks
6. autoregressive nodes.
7. line search and conjugate gradient optimization
The file describing a network is processed by the Aspirin parser and
files containing C functions to implement that network are generated.
This code can then be linked with an application which uses these
routines to control the network. Optionally, a complete simulation
may be automatically generated which is integrated with the MIGRAINES
interface and can read data in a variety of file formats. Currently
supported file formats are:
Ascii
Type1, Type2, Type3 Type4 Type5 (simple floating point file formats)
ProMatlab
Examples
- --------
A set of examples comes with the distribution:
xor: from RumelHart and McClelland, et al,
"Parallel Distributed Processing, Vol 1: Foundations",
MIT Press, 1986, pp. 330-334.
encode: from RumelHart and McClelland, et al,
"Parallel Distributed Processing, Vol 1: Foundations",
MIT Press, 1986, pp. 335-339.
bayes: Approximating the optimal bayes decision surface
for a gauss-gauss problem.
detect: Detecting a sine wave in noise.
iris: The classic iris database.
characters: Learing to recognize 4 characters independent
of rotation.
ring: Autoregressive network learns a decaying sinusoid
impulse response.
sequence: Autoregressive network learns to recognize
a short sequence of orthonormal vectors.
sonar: from Gorman, R. P., and Sejnowski, T. J. (1988).
"Analysis of Hidden Units in a Layered Network Trained to
Classify Sonar Targets" in Neural Networks, Vol. 1, pp. 75-89.
spiral: from Kevin J. Lang and Michael J, Witbrock, "Learning
to Tell Two Spirals Apart", in Proceedings of the 1988 Connectionist
Models Summer School, Morgan Kaufmann, 1988.
ntalk: from Sejnowski, T.J., and Rosenberg, C.R. (1987).
"Parallel networks that learn to pronounce English text" in
Complex Systems, 1, 145-168.
perf: a large network used only for performance testing.
monk: The backprop part of the monk paper. The MONK's problem were
the basis of a first international comparison
of learning algorithms. The result of this comparison is summarized in
"The MONK's Problems - A Performance Comparison of Different Learning
algorithms" by S.B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B.
Cestnik, J. Cheng, K. De Jong, S. Dzeroski, S.E. Fahlman, D. Fisher,
R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R.S.
Michalski, T. Mitchell, P. Pachowicz, Y. Reich H. Vafaie, W. Van de
Welde, W. Wenzel, J. Wnek, and J. Zhang has been published as
Technical Report CS-CMU-91-197, Carnegie Mellon University in Dec.
1991.
wine: From the ``UCI Repository Of Machine Learning Databases
and Domain Theories'' (ics.uci.edu: pub/machine-learning-databases).
Performance of Aspirin simulations
- ----------------------------------
The backpropagation code generator produces simulations
that run very efficiently. Aspirin simulations do
best on vector machines when the networks are large,
as exemplified by the Cray's performance. All simulations
were done using the Unix "time" function and include all
simulation overhead. The connections per second rating was
calculated by multiplying the number of iterations by the
total number of connections in the network and dividing by the
"user" time provided by the Unix time function. Two tests were
performed. In the first, the network was simply run "forward"
100,000 times and timed. In the second, the network was timed
in learning mode and run until convergence. Under both tests
the "user" time included the time to read in the data and initialize
the network.
Sonar:
This network is a two layer fully connected network
with 60 inputs: 2-34-60.
Millions of Connections per Second
Forward:
SparcStation1: 1
IBM RS/6000 320: 2.8
HP9000/720: 4.0
Meiko i860 (40MHz) : 4.4
Mercury i860 (40MHz) : 5.6
Cray YMP: 21.9
Cray C90: 33.2
Forward/Backward:
SparcStation1: 0.3
IBM RS/6000 320: 0.8
Meiko i860 (40MHz) : 0.9
HP9000/720: 1.1
Mercury i860 (40MHz) : 1.3
Cray YMP: 7.6
Cray C90: 13.5
Gorman, R. P., and Sejnowski, T. J. (1988). "Analysis of Hidden Units
in a Layered Network Trained to Classify Sonar Targets" in Neural Networks,
Vol. 1, pp. 75-89.
Nettalk:
This network is a two layer fully connected network
with [29 x 7] inputs: 26-[15 x 8]-[29 x 7]
Millions of Connections per Second
Forward:
SparcStation1: 1
IBM RS/6000 320: 3.5
HP9000/720: 4.5
Mercury i860 (40MHz) : 12.4
Meiko i860 (40MHz) : 12.6
Cray YMP: 113.5
Cray C90: 220.3
Forward/Backward:
SparcStation1: 0.4
IBM RS/6000 320: 1.3
HP9000/720: 1.7
Meiko i860 (40MHz) : 2.5
Mercury i860 (40MHz) : 3.7
Cray YMP: 40
Cray C90: 65.6
Sejnowski, T.J., and Rosenberg, C.R. (1987). "Parallel networks that
learn to pronounce English text" in Complex Systems, 1, 145-168.
Perf:
This network was only run on a few systems. It is very large
with very long vectors. The performance on this network
is in some sense a peak performance for a machine.
This network is a two layer fully connected network
with 2000 inputs: 100-500-2000
Millions of Connections per Second
Forward:
Cray YMP 103.00
Cray C90 220
Forward/Backward:
Cray YMP 25.46
Cray C90 59.3
MIGRAINES
- ------------
The MIGRAINES interface is a terminal based interface
that allows you to open Unix pipes to data in the neural
network. This replaces the NeWS1.1 graphical interface
in version 4.0 of the Aspirin/MIGRAINES software. The
new interface is not a simple to use as the version 4.0
interface but is much more portable and flexible.
The MIGRAINES interface allows users to output
neural network weight and node vectors to disk or to
other Unix processes. Users can display the data using
either public or commercial graphics/analysis tools.
Example filters are included that convert data exported through
MIGRAINES to formats readable by:
- Gnuplot 3
- Matlab
- Mathematica
- Xgobi
Most of the examples (see above) use the MIGRAINES
interface to dump data to disk and display it using
a public software package called Gnuplot3.
Gnuplot3 can be obtained via anonymous ftp from:
>>>> In general, Gnuplot 3 is available as the file gnuplot3.?.tar.Z
>>>> Please obtain gnuplot from the site nearest you. Many of the major ftp
>>>> archives world-wide have already picked up the latest version, so if
>>>> you found the old version elsewhere, you might check there.
>>>>
>>>> NORTH AMERICA:
>>>>
>>>> Anonymous ftp to dartmouth.edu (129.170.16.4)
>>>> Fetch
>>>> pub/gnuplot/gnuplot3.?.tar.Z
>>>> in binary mode.
>>>>>>>> A special hack for NeXTStep may be found on 'sonata.cc.purdue.edu'
>>>>>>>> in the directory /pub/next/submissions. The gnuplot3.0 distribution
>>>>>>>> is also there (in that directory).
>>>>>>>>
>>>>>>>> There is a problem to be aware of--you will need to recompile.
>>>>>>>> gnuplot has a minor bug, so you will need to compile the command.c
>>>>>>>> file separately with the HELPFILE defined as the entire path name
>>>>>>>> (including the help file name.) If you don't, the Makefile will over
>>>>>>>> ride the def and help won't work (in fact it will bomb the program.)
NetTools
- -----------
We have include a simple set of analysis tools
by Simon Dennis and Steven Phillips.
They are used in some of the examples to illustrate
the use of the MIGRAINES interface with analysis tools.
The package contains three tools for network analysis:
gea - Group Error Analysis
pca - Principal Components Analysis
cda - Canonical Discriminants Analysis
Analyze
- -------
"analyze" is a program inspired by Denis and Phillips'
Nettools. The "analyze" program does PCA, CDA, projections,
and histograms. It can read the same data file formats as are
supported by "bpmake" simulations and output data in a variety
of formats. Associated with this utility are shell scripts that
implement data reduction and feature extraction. "analyze" can be
used to understand how the hidden layers separate the data in order to
optimize the network architecture.
How to get Aspirin/MIGRAINES
- -----------------------
The software is available from two FTP sites, CMU's simulator
collection and UCLA's cognitive science machines. The compressed tar
file is a little less than 2 megabytes. Most of this space is
taken up by the documentation and examples. The software is currently
only available via anonymous FTP.
> To get the software from CMU's simulator collection:
1. Create an FTP connection from wherever you are to machine "pt.cs.cmu.edu"
(128.2.254.155).
2. Log in as user "anonymous" with password your username.
3. Change remote directory to "/afs/cs/project/connect/code". Any
subdirectories of this one should also be accessible. Parent directories
should not be. ****You must do this in a single operation****:
cd /afs/cs/project/connect/code
4. At this point FTP should be able to get a listing of files in this
directory and fetch the ones you want.
Problems? - contact us at "connectionists-request@cs.cmu.edu".
5. Set binary mode by typing the command "binary" ** THIS IS IMPORTANT **
6. Get the file "am6.tar.Z"
7. Get the file "am6.notes"
> To get the software from UCLA's cognitive science machines:
1. Create an FTP connection to "ftp.cognet.ucla.edu" (128.97.8.19)
(typically with the command "ftp ftp.cognet.ucla.edu")
2. Log in as user "anonymous" with password your username.
3. Change remote directory to "pub/alexis", by typing the command "cd pub/alexis"
4. Set binary mode by typing the command "binary" ** THIS IS IMPORTANT **
5. Get the file by typing the command "get am6.tar.Z"
6. Get the file "am6.notes"
Other sites
- -----------
If these sites do not work well for you, then try the archie
internet mail server. Send email:
To: archie@cs.mcgill.ca
Subject: prog am6.tar.Z
Archie will reply with a list of internet ftp sites
that you can get the software from.
How to unpack the software
- --------------------------
After ftp'ing the file make the directory you
wish to install the software. Go to that
directory and type:
zcat am6.tar.Z | tar xvf -
-or-
uncompress am6.tar.Z ; tar xvf am6.tar
How to print the manual
- -----------------------
The user documentation is located in ./doc in a
few compressed PostScript files. To print
each file on a PostScript printer type:
uncompress *.Z
lpr -s *.ps
Why?
- ----
I have been asked why MITRE is giving away this software.
MITRE is a non-profit organization funded by the
U.S. federal government. MITRE does research and
development into various technical areas. Our research
into neural network algorithms and applications has
resulted in this software. Since MITRE is a publically
funded organization, it seems appropriate that the
product of the neural network research be turned back
into the technical community at large.
Thanks
- ------
Thanks to the beta sites for helping me get
the bugs out and make this portable.
Thanks to the folks at CMU and UCLA for the ftp sites.
Copyright and license agreement
- -------------------------------
Since the Aspirin/MIGRAINES system is licensed free of charge,
the MITRE Corporation provides absolutely no warranty. Should
the Aspirin/MIGRAINES system prove defective, you must assume
the cost of all necessary servicing, repair or correction.
In no way will the MITRE Corporation be liable to you for
damages, including any lost profits, lost monies, or other
special, incidental or consequential damages arising out of
the use or in ability to use the Aspirin/MIGRAINES system.
This software is the copyright of The MITRE Corporation.
It may be freely used and modified for research and development
purposes. We require a brief acknowledgement in any research
paper or other publication where this software has made a significant
contribution. If you wish to use it for commercial gain you must contact
The MITRE Corporation for conditions of use. The MITRE Corporation
provides absolutely NO WARRANTY for this software.
Russell Leighton ^ / |\ /|
INTERNET: taylor@world.std.com |-| / | | |
| | / | | |
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End of Neuron Digest [Volume 12 Issue 1]
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