Fastest learning in small-world neural networks

摘要

We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We fmd that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, and pattern recognition. (C) 2005 Elsevier B.V. All rights reserved.