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Path: sparky!uunet!ukma!usenet.ins.cwru.edu!agate!ucbvax!CATTELL.PSYCH.UPENN.EDU!neuron-request From: neuron-request@CATTELL.PSYCH.UPENN.EDU ("Neuron-Digest Moderator") Newsgroups: comp.ai.neural-nets Subject: Neuron Digest V10 #18 (discussion + jobs + software) Message-ID: <863.721614085@cattell.psych.upenn.edu> Date: 13 Nov 92 00:21:25 GMT Sender: daemon@ucbvax.BERKELEY.EDU Reply-To: "Neuron-Request" <neuron-request@cattell.psych.upenn.edu> Distribution: world Organization: University of Pennsylvania Lines: 808 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 * * ------------------------------ End of Neuron Digest [Volume 10 Issue 18] *****************************************