Abstract
In this paper, we propose a novel multi-task learning algorithm based on an ensemble learning method. We consider a specific setting of the multi-task learning for binary classification problems, in which features are shared among all tasks and all tasks are targets of performance improvement. We focus on a situation that the shared structures among dataset are represented by divergence between underlying distributions associated with multiple tasks. We discuss properties of the proposed method and investigate validity of the proposed method with numerical experiments.
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.
