From: neuron-request@CATTELL.PSYCH.UPENN.EDU ("Neuron-Digest Moderator")
Newsgroups: comp.ai.neural-nets
Subject: Neuron Digest V10 #18 (discussion + jobs + software)
Date: 13 Nov 92 00:21:25 GMT
Reply-To: "Neuron-Request" <neuron-request@cattell.psych.upenn.edu>
Distribution: world
Organization: University of Pennsylvania

Neuron Digest   Thursday, 12 Nov 1992
                Volume 10 : Issue 18

Today's Topics:
                        Neural Nets Based Systems
                      Re: Neural Nets Based Systems
                      Re: Neural Nets Based Systems
                              Stock Market
                              Stock Market
                         Help on hybrid systems
                        Postdoc Position in Lund
         Free Neural Network Simulation and Analysis SW (am6.0)


Send submissions, questions, address maintenance, and requests for old
issues to "neuron-request@cattell.psych.upenn.edu". The ftp archives are
available from cattell.psych.upenn.edu (130.91.68.31). Back issues
requested by mail will eventually be sent, but may take a while.

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

Subject: Neural Nets Based Systems
From:    ehf@philabs.philips.com (Eberhard Fisch)
Organization: Philips Laboratories, Briarcliff, NY 10510
Date:    09 Nov 92 15:09:59 +0000

[[ Editor's Note: Given the recent post about a "stock market game," I
gleaned this message and the following two from a mailing list devoted to
general investing. Just For Your Information, of course. -PM ]]

I am interested in investigating the potential of neural networks for
forcasting markets. Im looking for articles or books dealing with the
application of neural nets for forcasting market moves or finding
patterns that occur in markets. I am not looking for, nor do I expect,
neural nets or any other model for market analysis to be a magic
solution. I just want to see whether neural network based systems have
merit.  Any opinions from people who have had some experience in using
some of the standard neural network software or have developed their own
software are welcome.

Eberhard Fisch
Philips Labs, NY

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

Subject: Re: Neural Nets Based Systems
From:    venky@thelonious.bellcore.com (G A Venkatesh)
Organization: Bellcore, Morristown NJ
Date:    09 Nov 92 18:56:52 +0000

Re: article by ehf@philabs.philips.com (Eberhard Fisch) writes:

I don't know where you could find more information about them but there are
at least two mutual funds (Fidelity Disciplined Equity and Fidelity Stock
Selector) that use a neural net program to help with their buy/sell decisions.
They seem to be doing well but I don't know how much the programs help.

venky

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

Subject: Re: Neural Nets Based Systems
From:    mincy@think.com (Jeffrey Mincy)
Organization: Thinking Machines Corporation, Cambridge MA, USA
Date:    09 Nov 92 19:13:21 +0000

Re: article by ehf@philabs.philips.com (Eberhard Fisch) writes:

The WSJ did the following article a few weeks ago:

"Fidelity's Bradford Lewis Takes Aim at Indexes With His 'neural Network'
Computer Program, by Robert Mcgough

... 
This fund is an index killer, he says of his fidelity disciplined equity fund.
Mr Lewis leaves the stock-picking to a "neural network," a computer program
that tries to mimic the intricate structure of the human brain.
...
Disciplined equity has beaten the S&P 500 by 2.3 to 5.6 percentage points in 
each of three years to 1991.  It's doint the same in the ytd with 5.8% return
...Only three other stock funds tracked by lipper did better than the market in
all four of these periods.
...

Anyway, perhaps this gives you some information.

 -- jeff
mincy@think.com

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

Subject: Stock Market
From:    D.Navin.Chandra@ISL1.RI.CMU.EDU
Date:    Sun, 08 Nov 92 20:26:40 -0500

[[ Editor's Note: See previous and following note about the use of Neural
Networks in the stock market.  It should be noted also that various "dart
board" investment strategoes regularly outperform the profession money
managers. See also Vol 10 #11 and #13 for related bibliographies. -PM ]]

Gary Bradsky

I agree the announcement was wrong, but please dont belittle the things
that other people try to work on. The Stock Market game is a legitimate
game entered into by 1000's of students from across the country. Many
people use NNets and GA's to "crack" the market trend. The area has also
been the most successful application of NN to date.

navin


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

Subject: Stock Market
From:    bradski@cns.bu.edu
Date:    Mon, 09 Nov 92 21:01:49 -0500

[[ Re: previous message from D.Navin.Chandra@ISL1.RI.CMU.EDU ]]

If the stock market game had not been a money making scheme, but simply
an "educational" contest as advertised, I would not have complained at
all.  I also don't personally care if some people make this type of
contest a money making business -- just let them advertise in magazines,
or TV, not in a news group (I get enough ads as it is).

Now, to add increasing amounts of content: 

(1) APPLICATIONS OF NN:
Stock market price prediction is not "the most successful application
of NN to date", the most successful application of neural networks is
clearly in adaptive filtering -- particularly for echo cancellation.
Adalines or their variants (lattice filters etc...) are used in nearly
every long distance phone line, modems, in EKG, EEG, ultra sound and
the list goes on.

(2) PREDICTION, STOCKS, AND SELECTIVE INFORMATION: 
Neuron readers ought to be aware, that even if someone "cracks" the
market with their NN application and wins a stock market contest, it
means absolutely nothing more than if someone won the contest by throwing
darts at the Wall St Journal and looking for the holes on the stock
quotation pages.  It means nothing more because we know nothing about the
statistical sample out of which we hear about the "winner" (and never the
losers).  If 5000 people try to predict stocks with backprop and one of
them wins big, and the rest fall around the mean of stock performance,
should you put your *future* money with the one winner?  Should you use
backprop?  Or, did the winner just happen to hit on a lucky fit of the
now, past data, that tells you nothing about future performance?

(3) PREDICTING MARKET TRENDS MAY BE IN NEED OF BELITTLEMENT: 
Trying to "crack the market trend" may in fact be hopeless, if by "trend"
one means the future expected value of stock prices.  If you are going to
use NNs with financial data, get an intro into financial theory (but be
prepared for a little math).  I suggest: Easy intro: John Hull, "Options,
Futures, and Other Derivative Securities", Prentice-Hall, 1989, More
cutting edge, but still decipherable: "Continuous-Time Finance", R.C.
Merton, Basil Blackwell, 1992 (get the cheaper paperback version).

In Continuous-Time Finance, read chapter 3 for an easy development of
stochastic calculus and Ito's lemma.  In chapter 3, Merton uses "order
statistics" to develop his equations (order statistics just tell you how
fast a term blows up or goes to zero).  On page 68-9 he develops a model
of stock market price changes based on an Ito process:

X(t) - X(t-1) = mean(t)*h + std_dev(t)*noise*h^(1/2)    (A)

where h is a small time increment which goes to zero in the limit, and
noise can be taken as zero-mean Gaussian.  The point of all this is that
in a small increment of time, the h^(1/2) "noise" or variance term
dominates the mean.  This is observed in practice, eg. the variability of
a stock's price swamps out the level, or expected value of its price.
Moreover to estimate the mean of (A) over n observations over a total
time period T (T = h*n), we'd use:

est_mean = Sum_{k=1 to n} (X(k) - X(k-1))/T = mean(t)

In other words, the estimate of the mean is not affected by choosing
finer observation intervals, only in the length of time "T" that we
measure over.  For estimating the variance of (A):

est_var = (Sum_{k=1 to n} (X(k) - X(k-1))^2/T = std_dev^2 + h*mean^2

                                          = std_dev^2 + (T/n)*mean^2

becomes better and better with finer measurements (larger n).

THE CONCLUSION: variances (or covariances) are easier to estimate from
stochastic time series than are the means.  This is a fundamental fact,
which no algorithm, no matter how neural or not, can overcome.

WHAT IT "MEANS": The stock and option models developed by Black-Scholes,
Merton and others have shown themselves to be useful (so useful in fact
that they are essentially what are keeping our large banks profitable
now). These models don't depend on having to know the mean, but do depend
on having to estimate the variance of stocks.  THUS, if you want to come
up with a useful NN financial application, try using NNs to estimate
stock variance, not stock prices.  You might start by using adaptive
filters as per (1) above to do this.

                                --Gary Bradski (bradski@cns.bu.edu)


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

Subject: Help on hybrid systems
From:    Fernando Passold <EEL3FPS%BRUFSC.bitnet@UICVM.UIC.EDU>
Organization: Universidade Federal de Santa Catarina/BRASIL
Date:    Thu, 12 Nov 92 17:37:23 -0300

[[ Editor's Note: I assume this person wanted his message to be
published.  If you, loyal reader, feel *your* reply to this fellow 
might be of more general interest, please send a copy to
neuron-request@cattell.psych.upenn.edu -PM ]]

    I would like to know the  intend of this  list  and if I
could obtain some replies.

    I am a researcher tacking part of Biomedical Engineering
Group Laboratory of Electrical Engineering Department at the
Federal University of  Santa  Catarina. We  develop   little
biomedical equipments  and  Expest Systems - such a Hospital
Infection Control Aid and a  Hybrid  System  (rule-based and
neural  network  based)  for  Proposal  and   Evaluation  of
Anesthesian Plan.

    I am a master degree student, now, grapple in develop  a
Expert Network  (also  called,  hybrid  system:  those  whom
combine  neural  networks   with   rule-based  methods)  for
Planning and Evaluation of  Plans of  Anesthesia, continuing
a  PHD  thesis,  for  Critical  Patients  (their  who   need
critical  cares) or Problem   Patients (exceptional cases of
patientes whom evaluate to critical patients).  I  have some
troubles choosing the  most  suitable  approach  to  develop
this system. I do not know if neural  networks could  be the
key to solve the main part of the problem,  because  we  are
dealing with  exceptions  that  probably could  best  solved
throught a rule-based method.  At  lately, there are  a  few
shells systems   applying  object-orienthed  technics   with
rule-based    or  frames  methods  such  the  new   Kappa PC
Application Development Systems   for   Windows environment,
from   IntellCorp.   Inc..     So,  I   am   interested   in
implementations   of  hybrid systems   using object-oriented
programming. Maybe, there will   be a  way to  link   neural
networks simulators   using   object-oriented   approach  to
another heuristical   languages such as Turbo-Prolog object-
oriented.

    I would be glad if someone  could make  a comment  about
it, as well as indicate if there exists a _similar research_.

    Please, reply directly to me  as I am not   subcribed to
this list.

    Thanks a lot respect to this matter,

best regards,

Fernando Passold
Biomedical Engineering Group Lab.
UFSC/BRAZIL
E-mail: ee3fps@brufsc.bitnet


P.S.: I am interested in subcribing in your list if not a
      large amount of material is normaly posted to it.


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

Subject: Postdoc Position in Lund
From:    carsten@thep.lu.se
Date:    Tue, 10 Nov 92 15:01:40 +0100

A two year postdoc position will be available within the Complex Systems
group at the Department of Theoretical Physics, University of Lund,
Sweden, starting September 1st 1993. The major research area of the group
is Artificial Neural Networks with tails into chaos and difficult
computational problems in general. Although some application studies
occur, algorithmic development is the focus in particular within the
following areas:

* Using Feed-back ANN for finding good solutions to combinatorial 
  optimization problems; knapsacks, scheduling, track-finding. 

* Time-series prediction.

* Robust multi-layer perceptron updating procedures including noise.

* Deformable template methods -- robust statistics.

* Configurational Chemistry -- Polymers, Proteins ...

* Application work within the domain of experimental physics, in particular 
  in connection with the upcoming SSC/LHC experiments.

Lund University is the largest campus in Scandinavia located in a
picturesque 1000 year old city (100k inhabitants). Lund is strategically
well located in the south of Sweden with 1.5 hrs commuting distance to
Copenhagen (Denmark).

The candidate should have a PhD in a relevant field, which need not be
Physics/Theoretical Physics.

Applications and three letters of recommendation should be sent to (not
later than December 15):

Carsten Peterson
Department of Theoretical Physics
University of Lund
Solvegatan 14A
S-223 62 Lund
Sweden

or

Bo S\"{o}derberg
Department of Theoretical Physics
University of Lund
Solvegatan 14A
S-223 62 Lund
Sweden


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

Subject: Free Neural Network Simulation and Analysis SW (am6.0)
From:    Russell R Leighton <taylor@world.std.com>
Date:    Fri, 30 Oct 92 09:09:54 -0500

 *************************************************************************
 **** delete all prerelease versions!!!!!!! (they are not up to date) ****
 *************************************************************************

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"

> To get the software from UCLA's cognitive science machines:

1. Create an FTP connection to "ftp.cognet.ucla.edu" (128.97.50.19)
(typically with the command "ftp ftp.cognet.ucla.edu")

2. Log in as user "anonymous" with password your username.

3. Change remote directory to "alexis", by typing the command "cd 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"

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.

October, 1992 


  Russell Leighton                                *     *                     
  MITRE Signal Processing Center      ***        ***   ***      ***           
  7525 Colshire Dr.                 ******       ***   ***    ******          
  McLean, Va. 22102, USA           *****************************************  
                                           *****    ***   ***        ******   
  INTERNET: taylor@world.std.com,            **     ***   ***          ***    
            leighton@mitre.org                       *     *                  



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End of Neuron Digest [Volume 10 Issue 18]
*****************************************
Newsgroups: comp.ai.neural-nets