An incremental learning algorithm that optimizes network size and sample size in one trial

A constructive learning algorithm is described that builds a feedforward neural network with an optimal number of hidden units to balance convergence and generalization. The method starts with a small training set and a small network, and expands the training set incrementally after training. If the training does not converge, the network grows incrementally to increase its learning capacity. This process, called selective learning with flexible neural architectures (SELF), results in a construction of an optimal size network for learning all the given data using only a minimal subset of them. The author shows that the network size optimization combined with active example selection generalizes significantly better and converges faster than conventional methods