Abstract
Bayesian Ying-Yang (BYY) harmony learning system is a newly developed framework for statistical learning. Via the BYY harmony leaning on finite mixtures, model selection can be made automatically during parameter learning. In this paper, this automated model selection learning mechanism is extended to logarithmic normal (log-normal) mixtures. Actually, an adaptive gradient BYY harmony learning algorithm is proposed for log-normal mixtures. It is demonstrated by the experiments that the proposed BYY harmony learning algorithm not only automatically determines the number of actual log-normal distributions in the sample dataset, but also leads to a satisfactory estimation of the parameters in the original log-normal mixture.
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.
