Gradient-based boosting for statistical relational learning: The relational dependency network case

@article{Natarajan2011GradientbasedBF,
  title={Gradient-based boosting for statistical relational learning: The relational dependency network case},
  author={Sriraam Natarajan and Tushar Khot and Kristian Kersting and Bernd Gutmann and Jude W. Shavlik},
  journal={Machine Learning},
  year={2011},
  volume={86},
  pages={25-56}
}
Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domains. This higher expressivity, however, comes at the expense of a more complex model-selection problem: an unbounded number of relational abstraction levels might need to be explored. Whereas current learning approaches for RDNs learn a single… CONTINUE READING

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