Fastest Learning in Small-World Neural Networks
نویسندگان
چکیده
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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This comment reexamines Simard et al.’s work in [D. Simard, L. Nadeau, H. Kröger, Phys. Lett. A 336 (2005) 8-15]. We found that Simard et al. calculated mistakenly the local connectivity lengths local D of networks. The right results of local D are presented and the supervised learning performance of feedforward neural networks (FNNs) with different rewirings are re-investigated in this comment...
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