ICML 2011 workshop on unsupervised
and transfer learning
Inductive transfer learning for Bayesian
Network structure learning
Alexandru
Niculescu-Mizil
NEC Labs, New
Jersey, USA
While receiving
significant attention in the machine learning community, Bayesian
Network structure learning remains challenging, especially when
training data is scarce. In this talk I show how structure learning
performance can be significantly improved through
inductive transfer, when data is available for multiple related
problems. I present a score and search algorithm for jointly
learning
multiple related Bayesian Networks that improves the quality of the
leaned dependency structures by transferring useful information among
the different related problems. I demonstrate the effectiveness of the
algorithm using two standard benchmark structure learning problems, and
a real bird ecology problem.