Structural learning about directed acyclic graphs from multiple databases. (English) Zbl 1256.68089

Summary: We propose an approach for structural learning of directed acyclic graphs from multiple databases. We first learn a local structure from each database separately, and then we combine these local structures together to construct a global graph over all variables. In our approach, we do not require conditional independence, which is a basic assumption in most methods.


68Q32 Computational learning theory
68P15 Database theory
68R10 Graph theory (including graph drawing) in computer science
68T05 Learning and adaptive systems in artificial intelligence


Full Text: DOI


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