Pruning product unit neural networks
A. Ismail, Andries Petrus Engelbrecht · 2003
Selection of the optimal architecture of a neural network is crucial to ensure good generalization by reducing the occurrence of overfitting. While much work has been done to develop pruning algorithms for networks that employ summation units, not much has been done on pruning of product unit neural networks. The paper develops and tests a pruning algorithm for product unit networks, and illustrates its performance on several function approximation tasks.