Abstract
Statistical Relational Learning (SRL) is concerned with building statistical models for relational data. While SRL approaches have shown much potential in complex real-world application domains, their computational complexity remains a major issue and often limits their practical applicability. This thesis is concerned with relatively simple yet efficient SRL techniques. We show how expressivity and generality can be traded for efficiency by restricting model complexity and developing special-purpose inference and learning algorithms that take advantage of such restrictions, as well as by tailoring models to specific application domains.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.