Path: utzoo!attcan!uunet!tut.cis.ohio-state.edu!purdue!bu-cs!slehar
From: slehar@bucasd.bu.edu (Lehar)
Newsgroups: comp.ai.neural-nets
Subject: Re: back-prop NNs and `SAS' regression!
Message-ID: <44929@bu-cs.BU.EDU>
Date: 19 Dec 89 15:00:55 GMT
References: <220700005@uxe.cso.uiuc.edu>
Sender: daemon@bu-cs.BU.EDU
Organization: Boston University Center for Adaptive Systems
Lines: 26


 HOW CAN ONE DEFEND THE USE OF NEURAL NETS WHEN BACKPROP DOES NO BETTER
 THAN POLYNOMIAL REGRESSION?

There are two distinct reasons for studying neural nets, the primary
reason is to gain insights into the mechanisms of natural
intelligence.  The secondary reason is that SOMETIMES neural nets can
solve problems more elegantly.  When this is the case, then it's
appropriate to use them.

If polynomial regression does better for your problem, then use
polynomial regression, it's sure to be faster, and is probably easier
to understand.  Backprop, like all neural nets, works best
(relatively) when the data is ambiguous, incomplete or noisy.

Remember that backprop is not the be-all and end-all of neural nets.
That honour (to date) goes to that big blob of jelly in your head.
Whenever you wonder whether neural nets are worth studying, think of
what you are wondering with.

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