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From: manj@brand.usc.edu (B. S. Manjunath)
Newsgroups: comp.ai.neural-nets
Subject: Re: back-prop NNs and `SAS' regression!
Keywords: Back Propagation, Approximation theory
Message-ID: <21845@usc.edu>
Date: 18 Dec 89 19:10:36 GMT
References: <220700005@uxe.cso.uiuc.edu>
Sender: news@usc.edu
Reply-To: manj@brand.usc.edu (B. S. Manjunath)
Organization: University of Southern California, Los Angeles, CA
Lines: 22

In article <220700005@uxe.cso.uiuc.edu> kbesrl@uxe.cso.uiuc.edu writes:
 >
 >
 >I have been experimenting with back-prop neural nets for the past
 >few months. I find that they are only as good as polynomial
 >regression. Actually, I ran a back-prop neural net on some
 >continuous mapping problems and found that they achieved the
 >same performance as the `SAS' statistical package.
 >
 >I am wondering whether this is true of other neural models.
 >If so, how can one defend the use of neural nets as opposed to
 >statistical regression. If someone can give me pointers to any
 >papers that discuss these aspects, it would be appreciated.
 >
 >sudha@kbesrl.me.uiuc.edu
 >sudhakar y. reddy

  You might be interested in a Technical report by T. Poggio and
F. Girosi , "A theory of Networks for Approximations and Learning"
 AI Memo #1140, M.I.T. AI Lab, July 1989.

B.S. Manjunath