Fastest learning in small-world neural networks |
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Affiliation: | Département de Physique, Université Laval, Québec, Québec G1K 7P4, Canada |
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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. |
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