I am currently trying to train my backpropagation to classify 150k training pairs. Each training pair is a vector of 18 Bipolar numbers and it runs through 2 hidden layers with a final output of 1 number(18-18-18-1).
When I feed my neural network with only a couple thousand training pairs it can classify them 100% but when I try to feed more it makes many errors. Does anyone have any recommendations for what can help a back prop net handle more training pairs? Should I do batch updating of weights or update them for every training pair(what I do now)? Also whats the best way to test for convergence with so many training pairs(when summing up error it's ~40k).Any help is much appreciated.
(FYI: each training pair is a loan account I am trying to evaluate with data on balance, age, how long it's been open, average income of zipcode and the target is whether or not it was paid)