小世界神经网络的快速学习
Fastest learning in small world neural networks
摘要：
我们研究神经网络中的监督学习。我们考虑一个多层前馈网络与反向传播。我们发现，小世界连接的网络减少了学习错误和学习时间相比，定期或随机连接的网络。该研究在数据挖掘、图像处理、语音识别和模式识别等领域具有潜在的应用价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
