Many data sets are relational in nature (e.g., citation graphs, the World Wide Web, genomic structures). These data offer unique opportunities to improve model accuracy, and thereby decision-making, if machine learning techniques can effectively exploit the relational information. To date research on statistical relational models has focused primarily on knowledge representation and inference--there has been little attention paid to the challenges and opportunities that are unique to learning in relational domains. This work will consider in depth the issue of structure learning and focus on developing accurate and efficient structure learning techniques for statistical relational models.
Subjects: 12. Machine Learning and Discovery
Submitted: Apr 4, 2005