Studies on an algorithm for reducing redundant units based on geometrical approach

摘要

This report studies how our proposed additional learning algorithm for structural learning with forgetting effects the structure determination of neural networks. The proposed learning algorithm eliminates redundant hidden units, which have the high similarity between hidden units, from neural networks. Prom simulation result performed on iris classification problem, it was confirmed that the proposed learning algorithm gives better network structure and higher generalization ability.