Fastest learning in small world neural networks
arXiv:physics/0402076 · doi:10.1016/j.physleta.2004.12.078
Abstract
We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We find 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.
Text completely revised (14 pages), all new figures (7 figs)