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From: demers@odin.ucsd.edu (David E Demers)
Newsgroups: comp.ai.neural-nets
Subject: Re: Why the more input neurons, the faster the convergence..?
Message-ID: <12433@sdcc6.ucsd.edu>
Date: 27 Aug 90 21:59:05 GMT
References: <5462@minyos.xx.rmit.oz>
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In article <5462@minyos.xx.rmit.oz> rcoahk@koel.co.rmit.oz (Alvaro Hui Kau) writes:
>From a recent experiment on Guassian data classification 
>using Bp Algorithm, I found that the higher dimensions
>ones( so need more input neurons) converge much much faster
>than those of lower dimensions.

>The order of difference is nearly 100 folds!

>I am wondering whether this is a general behavior of Bp nets,
>can anyone verify this for me.

>Of course, I use the same number of vector pairs in all case!

This should not be a surprising result.  The more degrees of freedom
you allow your model, the easier it should be to reduce error.  What
you might find, however, is that your net does not generalize well
to other inputs.  What the net is doing is building a smooth
function; and as we all know from function approximation, zero
error on the data does not necessarily mean we have a good model!

Dave