Rich,
You are generally right that a NN is basically a logistic regression.
Although one could get bogged down in an argument surrounding that.
To answer the original question. Brian Ripley's book on pattern recognition
and NN is not exactly right but I feel thats its in the right vein of
thinking.
It worth a look. I have seen a number of papers around the subject of NN and
Time Series.
Go to http://www.maths.uq.edu.au/~gks/webguide/index.html its an excellent
site and should help you in explorer the web for this question
Cheers
Mark Young
[EMAIL PROTECTED]
Rich Ulrich <[EMAIL PROTECTED]> wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> On Fri, 11 Feb 2000 15:01:25 GMT, [EMAIL PROTECTED] wrote:
>
> > I'm working on a study that compares neural networks to classical non-
> > linear statistical estimators in forecasting time series. My thesis is
> > that the NN would be robust under conditions where the assumptions of
> > the classical model are not met, and the nn would be inferior where the
> > classical assumptions are satisfied.
> >
> > What would be a good classical model to compare a neural network to?
> > Does anyone know of any papers/sources on this subject?
>
> Warren Sarle has written an FAQ on neural nets -- see the related
> Usenet groups, or see my FAQ for a reference to it.
>
> Basically... practically every NN *is* a classical model, so your
> question is not well-posed; it is fundamentally wrong in its
> assumptions. One NN is a logistic model, once you open up the black
> box. Another is simple discriminant function. And so on.
>
> --
> Rich Ulrich, [EMAIL PROTECTED]
> http://www.pitt.edu/~wpilib/index.html
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