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.