ABSTRACT Kernel extreme learning machine (KELM) introduces kernel leaning into extreme learning machine (ELM) in order to improve the generalization ability and stability. But the Penalty parameter in KELM is randomly set and it has a strong impact on the performance of KELM. A fast KELM combining the conjugate gradient method (CG-KELM) is presented in this paper. The CG-KELM computes the output weights of the neural network by the conjugate gradient iteration method. There is no penalty parameter to be set in CG-KELM. Therefore, the CG-KELM has good generalization ability and fast learning speed. The simulations in image restoration show that CG-KELM outperforms KELM. The CG-KELM provides a balanced method between KELM and ELM.

中文翻译：

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一种基于共轭梯度的快速核极限学习机

摘要 内核极限学习机（KELM）将内核倾斜引入极限学习机（ELM），以提高泛化能力和稳定性。但是KELM中的Penalty参数是随机设置的，对KELM的性能影响很大。本文提出了一种结合共轭梯度法（CG-KELM）的快速 KELM。CG-KELM 通过共轭梯度迭代法计算神经网络的输出权重。CG-KELM 中没有要设置的惩罚参数。因此，CG-KELM 具有良好的泛化能力和快速的学习速度。图像恢复中的模拟表明，CG-KELM 优于 KELM。CG-KELM 提供了 KELM 和 ELM 之间的平衡方法。