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From: ins_atge@jhunix.HCF.JHU.EDU (Thomas G Edwards)
Newsgroups: comp.ai.neural-nets
Subject: Re: Networks for pattern recognition problems?
Summary: Realism with NNs
Message-ID: <5856@jhunix.HCF.JHU.EDU>
Date: 18 Jul 90 17:11:37 GMT
References: <23586@boulder.Colorado.EDU>
Reply-To: ins_atge@jhunix.UUCP (Thomas G Edwards)
Organization: The Johns Hopkins University - HCF
Lines: 47

In article <23586@boulder.Colorado.EDU> fozzard@boulder.Colorado.EDU (Richard Fozzard) writes:
>I am working on a presentation to NOAA (National Oceanic and Atmospheric
>Admin.) management that partially involves pattern recognition
>and am trying to argue against the statement:
>"...results thus far [w/ networks] have not been notably more
>impressive than with more traditional pattern recognition techniques".

That's a difficult statement to argue against.  I do not recall any
neural network techniques for pattern recognition which _perform_
notably better than traditional pattern recognition techniques.

From my experience, these are the real advantage of neural nets:
1) Generality.  There are many general neural network systems
    which are capable of learning almost any kind of pattern
    recognition without much specialized knowledge of the programmer
    about the problem.
2) Speed of System Development.  Generalized neural models will
    enable a user to develop a categorization system very quickly.
    For example, I spend a week training a network to learn
    threat detection problems to an accuracy reached by months
    of signal analysis experts (I am sure, however, that they are on the
    way to developing more accurate systems in the near future)
3) High Speed VLSI implementation.  Trained networks can be implemented
    in a highly parallel manner in VLSI.  This, however, hasn't
    been done very much.  

In the future, it would be nice to expand the above list.  But for
right now, with commercially available software, that's about 
as far as I would go.  Neural Nets are currently an excellent way
to do a "quick job" of getting a lower bound on acceptable
pattern recognition ability.  In most cases, however, you would
probably want to start with Neural Nets, and then go beyond with
more advanced methods.  

Neural Nets are "thought savers."  They give you some very general
ability at relatively high speeds (on the order of days) without
you having to think about the problem.  They can be useful when
properly applied, and useless when improperly applied.

(I am aware of retina-like neural models which provide very
 good contrast enhancement and CCD element calibration which
 do work better than most "traditional" techniques...so
 there are some examples of neural networks being very useful.
 I am sure that as research into Neural Networks continue,
 they will become an ever increasing tool of science.)

-Thomas Edwards