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.
@article{arxiv.physics/0402076,
title = {Fastest learning in small world neural networks},
author = {D. Simard and L. Nadeau and H. Kröger},
journal= {arXiv preprint arXiv:physics/0402076},
year = {2009}
}
Comments
Text completely revised (14 pages), all new figures (7 figs)