reply on back-propagation fails to separate paper

[Richard Lippmann]

Geoff,
	We came up with the same conclusion a while ago when 
some people were worried about the performance of back propagation
but never published it. Back propagation with limits seems to converge
correctly for those contrived deterministic cases where minimizing 
total squared error does not minimize the percent patterns 
classified correctly. The limits cause the algorithm to change from
an LMS mean-square minimizing approach to perceptron-like 
error corrective approach. Typically, however, the difference in percent 
patterns classified correctly between the local and global 
solutions in those cases tends to be small. 
In practice, we found that convergence for the one contrived 
case we tested with limits took rather a long time. 

	I have never seen this published and it would be good to see 
your result published with a convergence proof.  I have also seen
little published on the effect of limits on performance of classifiers
or on final weight values.
	Rich