Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!samsung!usc!aero!abbott
From: abbott@aerospace.aero.org (Russell J. Abbott)
Newsgroups: comp.ai.neural-nets
Subject: What good are neural nets?
Message-ID: <68764@aerospace.AERO.ORG>
Date: 14 Mar 90 21:06:32 GMT
Reply-To: abbott@aero.UUCP (Russell J. Abbott)
Organization: The Aerospace Corporation, El Segundo, CA
Lines: 29


Is there a good characterization of the kinds of problems for which
neural nets are better than more traditional computational systems?
More specifically:

1) Is there a recognized (or even suggested) set of criteria in terms of
which one typically compares NN solutions to problems to more
traditional computational solutions?  Two possible criteria I can think
of are ease of development, e.g., training vs. programming, and speed of
execution once a system is developed.

2) Is there a characterization of a problem domain in which neural nets
are superior under any such criteria?


A related but somewhat different question:  to what extent are neural
nets equivalent to statistical classification algorithms?   That is, are
there neural nets that cannot be understood as instantiations of some
statistical classification algorithm?   In asking that question I want
to restrict the discussion to just the neural net part of a system and
not include a larger system that includes a neural net as a component.


What both of these questions are really getting at is the following.  If
a system designer wants to think of neural nets as one element in his
bag of system design tricks, what sort of function(s) should he think of
them as potentially capable of performing?
-- 
-- Russ abbott@itro3.aero.org