Learning from hints in neural networks

AbstractLearning from examples is the process of taking input-output examples of an unknown function ƒ and infering an implementation of ƒ. Learning from hints allows for general information about ƒ to be used instead of just input-output examples. We introduce a method for incorporating any invariance hint about ƒ in any descent method for learning from examples. We also show that learning in a neural network remains NP-complete with a certain, biologically plausible, hint about the network. We discuss the information value and the complexity value of hibts