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Fastest learning in small world neural networks

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 نشر من قبل Helmut Kroger
 تاريخ النشر 2004
  مجال البحث فيزياء
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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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