Generalization in Neural Networks

Generalization is the ability of a neural network to make accurate predictions when faced with data not drawn from the original training set (but drawn from the same source as the training set). Generalization is typically achieved by dividing available training data into three subsets; one used for training the network, one used to verify the performance of training algorithms as they run, and one to perform a final independent test.