An Enhanced Extreme Learning Machine Model for Improving Accuracy

This research paper focuses on optimization-based extreme learning machine (ELM), in order to enhance generalization ability and learning efficiency of ELM. The study explains new approach which enhances the efficiency of basic ELM algorithm and also reduces the problems of unmodified ELMs of generalization ability by reducing redundant information and low learning efficiency. In most cases, the proposed method can produce the generalization performance on the basis of a few classification and regression problems, and it was found to be competent. The proposed model has a greater potential of reducing the training time, improving the predictive accuracy and robustness. In the future, efforts will be made to attain a higher level of efficiency.