comp.ai.neural-nets FAQ: weekly reminder

This is a reminder for the existence of seven monthly postings to the
Usenet newsgroup comp.ai.neural-nets. The postings are called:
   comp.ai.neural-nets FAQ, Part 1 of 7: Introduction
   comp.ai.neural-nets FAQ, Part 2 of 7: Learning
   comp.ai.neural-nets FAQ, Part 3 of 7: Generalization
   comp.ai.neural-nets FAQ, Part 4 of 7: Books, data, etc.
   comp.ai.neural-nets FAQ, Part 5 of 7: Free software
   comp.ai.neural-nets FAQ, Part 6 of 7: Commercial software
   comp.ai.neural-nets FAQ, Part 7 of 7: Hardware and miscellaneous

FAQ stands for 'Frequently Asked Questions'.  Its purpose is to provide
basic information for individuals who are new to the field of neural
networks or are just beginning to read this group. It will help to
avoid lengthy discussion of questions that usually arise for beginners
of one or the other kind.

>>>>> SO, PLEASE, SEARCH THE FAQ POSTING FIRST IF YOU HAVE A QUESTION <<<<<

                           AND

>>>>> DON'T POST ANSWERS TO FAQs: POINT ASKERS TO THE FAQ POSTING <<<<<

The latest version of the FAQ is available as a hypertext document, readable
by any WWW (World Wide Web) browser such as Netscape, under the URL:

   ftp://ftp.sas.com/pub/neural/FAQ.html

This version is updated more frequently than the archived copies. All
seven parts of the FAQ can be downloaded from either of the following
URLS:

   ftp://ftp.sas.com/pub/neural/FAQ.html.zip
   ftp://ftp.sas.com/pub/neural/FAQ.txt.zip

The FAQ posting departs to comp.ai.neural-nets around the 28th of every
month. It is also sent to the groups comp.answers and news.answers
where it should be available at any time (ask your news manager).  The
FAQ posting, like any other posting, may a take a few days to find its
way over Usenet to your site. Such delays are especially common outside
of North America.

All changes to the FAQ from the previous month are shown in another
monthly posting having the subject `changes to "comp.ai.neural-nets FAQ"
-- monthly posting', which immediately follows the FAQ posting. The
`changes' post contains the full text of all changes and can also be
found at ftp://ftp.sas.com/pub/neural/changes.txt

If you are reading the version of the FAQ posted in comp.ai.neural-nets,
be sure to view it with a monospace font such as Courier. If you view
it with a proportional font, tables and formulas will be mangled.
Some newsreaders or WWW news services garble plain text. If you have
trouble viewing plain text, try the HTML version described above.

The FAQ posting is archived in the periodic posting archive on host
rtfm.mit.edu (and on some other hosts as well).  Look in the anonymous
ftp directory "/pub/usenet/news.answers/ai-faq/neural-nets".  The
filenames are "part1", "part2", ... "part7".  The full URLs are:
ftp://rtfm.mit.edu/pub/usenet/news.answers/ai-faq/neural-nets/part1
ftp://rtfm.mit.edu/pub/usenet/news.answers/ai-faq/neural-nets/part2
etc.

Other copies can be found on many servers around the world, although
many of them are years out of date. Here are some URLs that have fairly
recent copies:
http://www.faqs.org/faqs/ai-faq/neural-nets/
http://www.ge.infm.it/~corte/NN/FAQ.html 
http://www.creative.net.au/mirrors/neural/FAQ.html 

If you do not have anonymous ftp access, you can access the archive by
mail server as well.  Send an E-mail message to [EMAIL PROTECTED]
with "help" and "index" in the body on separate lines for more
information.  You can also go to http://www.dejanews.com/ and look for
posts containing "Neural Network FAQ" in comp.ai.neural-nets.

This reminder is posted every Sunday.

=====================================================================

Recent additions and changes to the FAQ :

Part 2: Learning
--- Why not code binary inputs as 0 and 1?

Part 4: Books, data, etc.
--- Books and articles about Neural Networks: added Kecman (2001),
    _Learning and Soft Computing: Support Vector Machines, Neural
    Networks, and Fuzzy Logic Models_

Part 7: Hardware and miscellaneous
--- Neural Network hardware: major overhaul, deleted borken links,
    added new links to review papers that are less out-of-date


=====================================================================

The following questions are answered in the FAQ postings:

Part 1: Introduction
--- What is this newsgroup for?  How shall it be used?
--- Where is comp.ai.neural-nets archived?
--- What if my question is not answered in the FAQ?
--- May I copy this FAQ?
--- What is a neural network (NN)?
--- Where can I find a simple introduction to NNs?
--- Are there any online books about NNs?
--- What can you do with an NN and what not?
--- Who is concerned with NNs?
--- How many kinds of NNs exist?
--- How many kinds of Kohonen networks exist? (And what is k-means?)
--- How are layers counted?
--- What are cases and variables?
--- What are the population, sample, training set,
    design set, validation set, and test set?
--- How are NNs related to statistical methods?

Part 2: Learning
--- What are combination, activation, error, and objective functions?
--- What are batch, incremental, on-line, off-line, deterministic,
    stochastic, adaptive, instantaneous, pattern, epoch, constructive,
    and sequential learning?
--- What is backprop?
--- What learning rate should be used for backprop?
--- What are conjugate gradients, Levenberg-Marquardt, etc.?
--- How does ill-conditioning affect NN training?
--- How should categories be coded?
--- Why not code binary inputs as 0 and 1?
--- Why use a bias/threshold?
--- Why use activation functions?
--- How to avoid overflow in the logistic function?
--- What is a softmax activation function?
--- What is the curse of dimensionality?
--- How do MLPs compare with RBFs?
--- What are OLS and subset/stepwise regression?
--- Should I normalize/standardize/rescale the data?
--- Should I nonlinearly transform the data?
--- How to measure importance of inputs?
--- What is ART?
--- What is PNN?
--- What is GRNN?
--- What does unsupervised learning learn?
--- Help! My NN won't learn! What should I do?        

Part 3: Generalization
--- How is generalization possible?
--- How does noise affect generalization?
--- What is overfitting and how can I avoid it?
--- What is jitter? (Training with noise)
--- What is early stopping?
--- What is weight decay?
--- What is Bayesian learning?
--- How to combine networks?
--- How many hidden layers should I use?
--- How many hidden units should I use?
--- How can generalization error be estimated?
--- What are cross-validation and bootstrapping?
--- How to compute prediction and
    confidence intervals (error bars)?

Part 4: Books, data, etc.
--- Books and articles about Neural Networks?
--- Any journals and magazines about Neural Networks?
--- Conferences and Workshops on Neural Networks?
--- Neural Network Associations?
--- Mailing lists, BBS, CD-ROM?
--- How to benchmark learning methods?
--- Databases for experimentation with NNs?

Part 5: Free software
--- Source code on the web?
--- Freeware and shareware packages for NN simulation?

Part 6: Commercial software
--- Commercial software packages for NN simulation?

Part 7: Hardware and miscellaneous
--- Neural Network hardware?
--- What are some applications of NNs?
--- What to do with missing/incomplete data?
--- How to forecast time series (temporal sequences)?
--- How to learn an inverse of a function? 
--- How to get invariant recognition of images under translation,
    rotation, etc.?
--- How to recognize handwritten characters?
--- What about Genetic Algorithms and Evolutionary Computation?
--- What about Fuzzy Logic?
--- Unanswered FAQs
--- Other NN links?

========================================================================
-- 

Warren S. Sarle       SAS Institute Inc.   The opinions expressed here
[EMAIL PROTECTED]    SAS Campus Drive     are mine and not necessarily
(919) 677-8000        Cary, NC 27513, USA  those of SAS Institute.