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From: rcoahk@koel.co.rmit.oz (Alvaro Hui Kau)
Newsgroups: comp.ai.neural-nets
Subject: Why the more input neurons, the faster the convergence..?
Message-ID: <5462@minyos.xx.rmit.oz>
Date: 24 Aug 90 01:56:27 GMT
Sender: news@minyos.xx.rmit.oz
Lines: 30


Hi, all experts:

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!


Neural-nets are fun if we all have supercomputers....
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	Alvaro Hui		|ACSnet		akkh@mullian.oz
    4th Year B.E.\ B.Sc.	|Internet &	akkh@mullian.ee.mu.OZ.AU
   University of Melbourne	|Arpanet	rcoahk@koel.co.rmit.OZ.AU 
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	Alvaro Hui		|ACSnet		akkh@mullian.oz
    4th Year B.E.\ B.Sc.	|Internet &	akkh@mullian.ee.mu.OZ.AU
   University of Melbourne	|Arpanet	rcoahk@koel.co.rmit.OZ.AU