From: prechelt@i41s14.ira.uka.de (Lutz Prechelt)
Newsgroups: comp.ai.neural-nets
Subject: changes to "FAQ in comp.ai.neural-nets" -- monthly posting
Supersedes: <nn.changes.posting_704427486@i41s14.ira.uka.de>
Followup-To: comp.ai.neural-nets
Date: 28 Aug 1992 02:20:32 GMT
Organization: University of Karlsruhe, Germany
Expires: 2 Oct 1992 02:18:13 GMT
Reply-To: prechelt@ira.uka.de (Lutz Prechelt)
NNTP-Posting-Host: i41s14.ira.uka.de
Keywords: modifications, new, additions, deletions
Originator: prechelt@i41s14

*** nn.oldbody	Tue Jul 28 04:18:07 1992
--- nn.body	Fri Aug  7 16:37:07 1992
***************
*** 1,5 ****
  
  Archive-name: neural-net-faq
! Last-modified: 92/07/13
  
  (FAQ means "Frequently Asked Questions")
--- 1,5 ----
  
  Archive-name: neural-net-faq
! Last-modified: 92/08/07
  
  (FAQ means "Frequently Asked Questions")
***************
*** 128,132 ****
    Such a summary should be announced in the original posting of the question
    or request with a phrase like
!     "Please email, I'll summarize"
  
    In such a case answers should NOT be posted to the newsgroup but instead
--- 128,132 ----
    Such a summary should be announced in the original posting of the question
    or request with a phrase like
!     "Please answer by email, I'll summarize"
  
    In such a case answers should NOT be posted to the newsgroup but instead
***************
*** 201,212 ****
     and weights; only local information; highly parallel operation ]
  
  A vague description is as follows:
- A NN is a network of many very simple processors ("units"), each with a 
- (small amount of) local memory, who are connected by unidirectional
- communication channels ("connections"), which carry numeric (as 
- opposed to symbolic) data.
- The units operate only on their local data and on the inputs
- they receive via the connections.
  
  The design motivation is what distinguishes neural networks from other
  mathematical techniques:
--- 201,219 ----
     and weights; only local information; highly parallel operation ]
  
+ First of all, when we are talking about a neural network, we *should*
+ usually better say "artificial neural network" (ANN), because that is
+ what we mean  most of the time. Biological neural networks are much
+ more complicated in their elementary structures than the mathematical
+ models we use for ANNs.
+ 
  A vague description is as follows:
  
+ An ANN is a network of many very simple processors ("units"), each
+ possibly having a (small amount of) local memory. The units are
+ connected by unidirectional communication channels ("connections"),
+ which carry numeric (as opposed to symbolic) data.  The units operate
+ only on their local data and on the inputs they receive via the
+ connections.
+ 
  The design motivation is what distinguishes neural networks from other
  mathematical techniques:
***************
*** 286,295 ****
  in layers) feedforward (i.e., the arcs joining nodes are
  unidirectional, and there are no cycles) nets.
- Back-propagation assumes knowledge of the right answer from a teacher and
- uses gradient descent on the error (provided by the teacher) to train the
- weights.
- The activation function is (usually) a sigmoidal (i.e., bounded above and
- below, but differentiable) function of a weighted sum of the nodes inputs.
  
  The use of a gradient descent algorithm to train its weights makes it
  slow to train; but being a feedforward algorithm, it is quite rapid during
--- 293,303 ----
  in layers) feedforward (i.e., the arcs joining nodes are
  unidirectional, and there are no cycles) nets.
  
+ Back-propagation needs a teacher that knows the correct output for any
+ input ("supervised learning") and uses gradient descent on the error
+ (as provided by the teacher) to train the weights.  The activation
+ function is (usually) a sigmoidal (i.e., bounded above and below, but
+ differentiable) function of a weighted sum of the nodes inputs.
+ 
  The use of a gradient descent algorithm to train its weights makes it
  slow to train; but being a feedforward algorithm, it is quite rapid during
***************
*** 301,305 ****
    Microstructure of Cognition (volume 1, pp 318-362). 
    The MIT Press. 
! (this is the classic one) or one of the some thousand other books 
  or articles on backpropagation  :->
  
--- 309,313 ----
    Microstructure of Cognition (volume 1, pp 318-362). 
    The MIT Press. 
! (this is the classic one) or one of the dozens of other books 
  or articles on backpropagation  :->
  
***************
*** 458,461 ****
--- 466,479 ----
  understand".
  
+ McClelland, J. L. and Rumelhart, D. E. (1988).
+ Explorations in Parallel Distributed Processing: Computational Models of 
+ Cognition and Perception (software manual). The MIT Press.
+ Comments: "Written in a tutorial style, and includes 2 diskettes of NN 
+ simulation programs that can be compiled on MS-DOS or Unix (and they do 
+ too !)"; "The programs are pretty reasonable as an introduction to some 
+ of the things that NNs can do."; "There are *two* editions of this book.  
+ One comes with disks for the IBM PC, the other comes with disks for the 
+ Macintosh".
+ 
  McCord Nelson, M. and Illingworth, W.T. (1990). A Practical Guide to Neural 
  Nets. Addison-Wesley Publishing Company, Inc. (ISBN 0-201-52376-0).
***************
*** 560,573 ****
  applications implementation".
  
- McClelland, J. L. and Rumelhart, D. E. (1988).
- Explorations in Parallel Distributed Processing: Computational Models of 
- Cognition and Perception (software manual). The MIT Press.
- Comments: "Written in a tutorial style, and includes 2 diskettes of NN 
- simulation programs that can be compiled on MS-DOS or Unix (and they do 
- too !)"; "The programs are pretty reasonable as an introduction to some 
- of the things that nns can do."; "There are *two* editions of this book.  
- One comes with disks for the IBM PC, the other comes with disks for the 
- Macintosh".
- 
  Pao, Y. H. (1989). Adaptive Pattern Recognition and Neural Networks
  Addison-Wesley Publishing Company, Inc. (ISBN 0-201-12584-6)
--- 578,581 ----
***************
*** 973,976 ****
--- 981,987 ----
  2. Usenet groups comp.ai.neural-nets (Oha !  :-> )
     and comp.theory.self-org-sys
+    There is a periodic posting on comp.ai.neural-nets sent by
+    srctran@world.std.com (Gregory Aharonian) about Neural Network 
+    patents.
  
  3. Central Neural System Electronic Bulletin Board
***************
*** 1016,1020 ****
       (source code:)     rcs_v4.2.justsrc.tar.Z   (1.4 MB)
  
- 
  2. UCLA-SFINX
     ftp 131.179.16.6  (retina.cs.ucla.edu)
--- 1027,1030 ----
***************
*** 1158,1162 ****
       Models, Programs, and Exercises" by McClelland and Rumelhart.
       MIT Press, 1988.
!    This book is often referred to as PDP vol III which is a very
     misleading practice!  The book comes with software on an IBM disk but
     includes a makefile for compiling on UNIX systems.  The version of
--- 1168,1172 ----
       Models, Programs, and Exercises" by McClelland and Rumelhart.
       MIT Press, 1988.
!    Comment: "This book is often referred to as PDP vol III which is a very
     misleading practice!  The book comes with software on an IBM disk but
     includes a makefile for compiling on UNIX systems.  The version of
***************
*** 1163,1167 ****
     PDP available at nic.funet.fi seems identical to the one with the book
     except for a bug in bp.c which occurs when you try to run a script of
!    PDP commands using the DO command.  This can be found and fixed easily.
  
  16. Xerion
--- 1173,1177 ----
     PDP available at nic.funet.fi seems identical to the one with the book
     except for a bug in bp.c which occurs when you try to run a script of
!    PDP commands using the DO command.  This can be found and fixed easily."
  
  16. Xerion
-- 
Lutz Prechelt   (email: prechelt@ira.uka.de)            | Whenever you 
Institut fuer Programmstrukturen und Datenorganisation  | complicate things,
Universitaet Karlsruhe;  D-7500 Karlsruhe 1;  Germany   | they get
(Voice: ++49/721/608-4317, FAX: ++49/721/694092)        | less simple.