Abstract
Abstract Based on Gaussian margin machine (GMM) and extreme learning machine (ELM), confidence-weighted ELM (CW-ELM) is proposed to provide point forecasts and confidence intervals. CW-ELM maintains a multivariate normal distribution over the output weight vector. It is applied to seek the least informative distribution from those that keep the targets within the forecast confidence intervals. For simplicity, the covariance matrix is assumed to be diagonal. The simplified problem of CW-ELM is approximately solved by using Leave-One-Out-Incremental ELM (LOO-IELM) and the interior point method. Our experimental results on both synthetic and real-world regression datasets demonstrate that CW-ELM has better performance than Bayesian ELM and Gaussian process regression.
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.
