a College of Information Engineering , Xiangtan University , Xiangtan , China.
b College of Computer Science and Engineering , NUST , Nanjing , China.
Network. 2018;29(1-4):70-80. doi: 10.1080/0954898X.2018.1562247. Epub 2019 Jan 27.
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.
核极端学习机（KELM）将核学习引入极端学习机（ELM）中，以提高泛化能力和稳定性。但是，KELM 中的惩罚参数是随机设置的，它对 KELM 的性能有很大的影响。本文提出了一种结合共轭梯度法的快速 KELM（CG-KELM）。CG-KELM 通过共轭梯度迭代法计算神经网络的输出权值。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。图像恢复的仿真表明，CG-KELM 优于 KELM。CG-KELM 为 KELM 和 ELM 之间提供了一种平衡的方法。
