The problem I have is with understanding how committee machines work. The only relevant info I find in this article is this:

â€śThe neural networks are executed simulta-

neously for the given input data and their outputs are

evaluated and combined to produce the final committee

output to obtain better generalization and performance. The

output combination module was often performed based on

simple functions on the outputs of individual members in

the committee machine, such as majority voting for clas-

sification and simple/weighted averaging for regression,

without involving the input vectors of attributesâ€ť

So what does that mean, exactly? I have 10 different neural networks, compute their result, average them, and use that average in error estimation and then do backprop? Or do I simply train 10 different neural networks, then run my test sample through all of them and average the result?