Increased generalization through selective decay in a constructive cascade network

Determining the optimum amount of regularization to obtain the best generalization performance in feedforward neural networks is a difficult problem, and is a form of the bias-variance dilemma. This problem is addressed in the CasPer algorithm, a constructive cascade algorithm that uses weight decay. Previously the amount of weight decay used by this algorithm was set by a parameter prior to training, often by trial and error. This is overcome through the use of a pool of neurons which are candidates for insertion into the network. Each neuron in the pool has an associated decay level, and the one which produces the best generalization on a validation set is added to the network. This not only removes the need for the user to select a decay value, but results in better generalization compared to networks with fixed, user optimized, decay values