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

Trading expressivity for efficiency in statistical relational learning: Ph.D. thesis abstract,10.1145/1809400.1809410,Sigkdd Explorations,Niels Landwe

Trading expressivity for efficiency in statistical relational learning: Ph.D. thesis abstract  
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Statistical Relational Learning (SRL) is concerned with build- ing statistical models for relational data. While SRL ap- proaches 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 ecient SRL techniques. We show how expressivity and generality can be traded for eciency by restricting model complexity and developing special-purpose inference and learning algo- rithms that take advantage of such restrictions, as well as by tailoring models to specic application domains .
Journal: Sigkdd Explorations , vol. 11, no. 2, pp. 59-60, 2009
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