Spatial crosstalk and modular NN architechture

[Patrick van der Smagt]

>         I have to model a problem with 28 discrete inputs(1's and 0's) and
>26 discrete outputs. Infact, these 26 discrete outputs can be represented by
>5 normalized continous outputs also.

If one would want to model any kind of function, why go for the least
obvious solution via a neural network first?  Since your problem is
binary, too, I would first create a much simpler method such as 
k-nearest-neighbour or any bin approach which would enable one to
gain an understanding of the data and the overlap.  Ten years ago
this would have been a more standard approach, instead of using a
black box (aka neural network).

The reason that I would _not_ immediately grasp a network to do some
function-approximation is that I have seen too many people choke
on the fact that they do not understand their data, or the complexity
of the data, a reasonable ratio #degrees of freedom:#learning samples,
etc.

				Patrick van der Smagt