| Autor: |
Schulte, Oliver, Qian, Zhensong, Kirkpatrick, Arthur, Yin, Xiaoqian, Sun, Yan |
| Předmět: |
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| Zdroj: |
Machine Learning; Jun2016, Vol. 103 Issue 3, p377-406, 30p |
| Abstrakt: |
A relational dependency network (RDN) is a directed graphical model widely used for multi-relational data. These networks allow cyclic dependencies, necessary to represent relational auto-correlations. We describe an approach for learning both the RDN's structure and its parameters, given an input relational database: First learn a Bayesian network (BN), then transform the Bayesian network to an RDN. Thus fast Bayesian network learning translates into fast RDN learning. The BN-to-RDN transform comprises a simple, local adjustment of the Bayesian network structure and a closed-form transform of the Bayesian network parameters. This method can learn an RDN for a dataset with a million tuples in minutes. We empirically compare our approach to a state-of-the-art RDN learning approach that applies functional gradient boosting, using six benchmark datasets. Learning RDNs via BNs scales much better to large datasets than learning RDNs with current boosting methods. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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