Extreme learning machine (ELM) has been put forward for single hidden layer feedforward networks. Because of its powerful modeling ability and it needs less human intervention, the ELM algorithm has been used widely in both regression and classification experiments. However, in order to achieve required accuracy, it needs many more hidden nodes than is typically needed by the conventional neural networks. This paper considers a new efficient learning algorithm for ELM with smoothing regularization. A novel algorithm updates weights in the direction along which the overall square error is reduced the most and then this new algorithm can sparse network structure very efficiently. The numerical experiments show that the ELM algorithm with smoothing regularization has less hidden nodes but better generalization performance than original ELM and ELM with regularization algorithms.

中文翻译：

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极限学习机的平滑正则化

极限学习机（ELM）已针对单隐藏层前馈网络提出。由于其强大的建模能力和较少的人工干预，ELM算法已被广泛用于回归和分类实验中。但是，为了获得所需的准确性，它需要比常规神经网络通常需要的隐藏节点更多的节点。本文考虑一种具有平滑正则化的ELM高效学习算法。一种新颖的算法在最大程度减少总体平方误差的方向上更新权重，然后该新算法可以非常有效地稀疏网络结构。数值实验表明，采用平滑的ELM算法与原始ELM和带有正则化算法的ELM相比，正则化具有较少的隐藏节点，但具有更好的泛化性能。