Trading expressivity for efficiency in statistical relational learning: Ph.D. thesis abstract

Niels Landwehr

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.