Abstract
As a more compact network, sparse ELM is an alternative model of extreme learning machine (ELM) for classification, which requires less memory space and testing time than conventional ELMs. In this paper, a fast training algorithm (FTA-sELM) specially developed for sparse ELM is proposed, which improves training speed, achieves better generalization performance and further promotes the application of sparse ELM in large data problem. The proposed algorithm breaks the large quadratic programming (QP) problem of sparse ELM into a series of two-dimensional sub-QP problems, specifically. In every iteration, Newton’s method is employed to solve the optimal solution for each sub-QP problem. Moreover, a new clipping scheme for Lagrange multipliers is presented, which improves convergence performance.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
