A fast kernel extreme learning machine based on conjugate gradient

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.