Title: Gradient-based boosting for statistical relational learning: the relational dependency network case
Authors: Natarajan, Sriraam ×
Khot, Tushar
Kersting, Kristian
Gutmann, Bernd
Shavlik, Jude #
Issue Date: 2012
Publisher: Springer New York LLC
Series Title: Machine Learning vol:86 issue:1 pages:25-56
Conference: International Conference on Inductive Logic Programming (ILP) edition:20 location:Florence, Italy date:27-30 June 2010
Abstract: 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 probability tree per random variable, we propose to turn the problem into 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. Our experimental results in several different data sets show that this boosting method results in efficient learning of RDNs when compared to state-of-the-art statistical relational learning approaches.
ISSN: 0885-6125
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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