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From: demers@odin.ucsd.edu (David E Demers)
Newsgroups: comp.ai.neural-nets
Subject: Re: Distinguishing "Normal" from "Abnormal" Data
Message-ID: <11976@sdcc6.ucsd.edu>
Date: 22 Jul 90 19:54:02 GMT
References: <64712@lll-winken.LLNL.GOV> <3071@osc.COM>
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In article <3071@osc.COM> jgk@osc.COM (Joe Keane) writes:
>In article <64712@lll-winken.LLNL.GOV> loren@tristan.llnl.gov (Loren Petrich)
>writes:
[about a classification problem...]
>This may be heresy in comp.ai.neural-nets, but this task seems ideally suited
>to standard statistical analysis.  
[...]
>Don't get me wrong, i think neural nets are very interesting, and they have
>produced good results in some areas.  But i see them being used where more
>mundane methods would work quite well, and probably much faster.

>It seems like NN is the newest trick, so people want to use it everywhere.
>But in the process they don't hear about the old things, which is too bad.  Is
>it just me, or are others bothered by this trend?

Not everyone knows all about what has been done.  And two months
in the lab will save two hours in the library...:-)

I agree that there are a lot of people in a lot of fields who
attempt to use a tool that is not appropriate for their problem,
out of ignorance of what the right tool is.  

Neural networks can capture high order statistics about a dataset
that are difficult to get with conventional methods.  However,
many problems don't need to be solved with nonlinear regression
and simpler, well-tested, fast methods may be best.

Yes, I think that there are a lot of papers applying nets to
inappropriate problems, but I am not bothered by it.  Eventually
they will learn about other, perhaps superior, approaches;
possibly from reading the net if not from peer review...

Dave