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From: cs_leo@ug.cs.ust.hk (Wong Tsz Cheong)
Subject: Q. of BPNN
Message-ID: <1995Feb22.125046.19680@uxmail.ust.hk>
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Date: Wed, 22 Feb 1995 12:50:46 GMT
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Hi,
	As I am doning the project about face recognition in Neural Network,
	I have encountered some problems:

1) By some books, the weights are adjusted when the training set is trained
   once and the average sum-squared error is larger than the required maximum
   error.
   However, I have found a example BPNN program had a different idea.
   It is that the weights are adjusted when one pattern(in the training set)
   is trained once and the sum-squared error of this pattern is larger than
   maximum error.

   Could someone tell me why it works and which one is better?(e.g. errors
   decrease faster.)


2) I have put the required faces patterns in the training set. However, it
   is not enough.  If a face which is not in our database is input in NN,
   it should tell me it is not.  So, I need to train some error patterns
   (means the faces not in our database) or garbage. But I can't include a
   large number of error patterns since it will increase the training set's
   size and also the number of hidden nodes. If a few error patterns are
   included, the percentage of incorrect recognition will be large.

   Could someone suggest some guidelines of training set for me?



I'll appreciate your help.
Thanks!

Leo
