Hopefully the last NN question you'll get from me this weekend, but here goes :)
Is there a way to handle an input that you "don't always know"... so it doesn't affect the weightings somehow?
Soo... if I ask someone if they are male or female and they would not like to answer, is there a way to disregard this input? Perhaps by placing it squarely in the centre? (assuming 1,0 inputs at 0.5?)
Thanks
Neural networks are fairly resistant to noise - that's one of their big advantages. You may want to try putting inputs at (-1.0,1.0) instead, with 0 as the non-input input, though. That way the input to the weights from that neuron is 0.0, meaning that no learning will occur there.
Probably the best book I've ever had the misfortune of not finishing (yet!) is Neural Networks and Learning Machines by Simon S. Haykin. In it, he talks about all kinds of issues, including the way you should distribute your inputs/training set for the best training, etc. It's a really great book!
You probably know this or suspect it, but there's no statistical basis for guessing or supplying the missing values by averaging over the range of possible values, etc.
For NN in particular, there are quite a few techniques avaialble. The technique i use--that i've coded--is one of the simpler techniques, but it has a solid statistical basis and it's still used today. The academic paper that describes it here.
The theory that underlies this technique is weighted integration over the incomlete data. In practice, no integrals are evaluated, instead they are approximated by closed-form solutions of Gaussian Basis Function networks. As you'll see in the paper (which is a step-by-step explanation, it's simple to implement in your backprop algorithm.