摘要：Local Coupled Extreme Learning Machine (LCELM) is a recently-proposed variant of ELM, which assigns an address for each hidden-layer node and activates the hidden-layer node when its activated degree is less than a given threshold. In this paper, an improved version of LCELM is proposed by developing a new way to initialize the address for each hidden-layer node and calculating the activated degree of hidden-layer node with Gaussian kernel. The experimental comparison with ELM and LCELM demonstrates the feasibility and effectiveness of improve LCELM which obtains the higher testing accuracy without significantly increasing the training time of ELM.
