Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domains where the joint probability distribution over the variables is approximated as a product of conditional distributions. This higher expressivity, however, comes at the expense of 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 probability tree per random variable, RDN-Boost learns a series of relational function-approximation problems using gradient-based boosting In doing so, one can easily induce highly complex features over several iterations and in turn estimate quickly a very expressive model.

By default, if multiple target predicates are provided, the code learns a relational dependency network.

The software provided here can also learn a single relational probability tree with -noBoost flag though the main contribution is the functional gradient boosting of RDNs.

For more details on learning RDN please refer to

Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann and Jude Shavlik. Gradient-based Boosting for Statistical Relational Learning: The Relational Dependency Network Case, Special issue of Machine Learning Journal (MLJ), Volume 86, Number 1, 25-56, 2012.