Abstract
This paper proposes an adaptive network architecture called Hybrid SOLAR (Supervised One-shot Learning Algorithm for Real number inputs), which is a hybrid algorithm between a one-shot network construction algorithm and an iterative pruning algorithm. Hybrid SOLAR determines a network structure in two stages. In the first stage, Hybrid SOLAR requires only a single presentation of training examples to construct the network and learning is finished. In the second stage, the network prunes redundant weights to improve the generalization ability. Thus, Hybrid SOLAR retains the advantages of those two algorithms. It needs only a single presentation of the training set for learning and the generalization ability is satisfactory.
