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From: joshi@wuche2.wustl.edu (Amol Joshi)
Newsgroups: comp.ai.neural-nets
Subject: Re: back-prop NNs and `SAS' regression!
Message-ID: <1989Dec19.172314.16051@wuche2.wustl.edu>
Date: 19 Dec 89 17:23:14 GMT
References: <220700005@uxe.cso.uiuc.edu> <1989Dec18.210859.23621@wuche2.wustl.edu> <21539@uflorida.cis.ufl.EDU>
Reply-To: joshi@wuche2.UUCP (Amol Joshi)
Organization: Washington University in St. Louis
Lines: 27

In article <21539@uflorida.cis.ufl.EDU> fishwick@fish.cis.ufl.edu (Paul Fishwick) writes:
>You say that in regression that the "structure of the model be prefixed"
>however I will debate this assumption -- the structure of a set of
>equations is no more prefixed than a neural network model. A neural
>network is a set of equations shown in a graphical syntactic form.
>It is just as easy to add and delete terms/equations as it is to add/delete
>nodes, etc. The equational equivalent of removing a link is to make
>zero a parameter.

by "structure" of a nonlinear model i mean also the nature of the non-linearities.
when doing non-linear least sqaure fit,e.g., i have to specify what exactly these
terms look like (exponential, hyperbolic etc..). so, i would use regression for
finding out "best" parameters for an existing analytical model.
the BP-nn equivalent of the complexity of the model is, i think, the parameters
like the number of layers, number of nodes in each layer - and yes, you need to
fiddle with those. the advantage with regression is that, if it works, it provides
insight to the physical system. BP-nn is more like a black-box and extracting
knowledge like the exponential dependencies etc is impossible from the information
about weights alone. am i missing something?
:amol


-- 
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Amol Joshi
Department of Chemical Engineering