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Daphne Koller Publications

Active Learning for Parameter Estimation in Bayesian Networks (2001)

by S. Tong and D. Koller


Abstract: Bayesian networks are graphical representations of probability distributions. In virtually all of the work on learning these networks, the assumption is that we are presented with a data set consisting of randomly generated instances from the underlying distribution. In many situations, however, we also have the option of active learning, where we have the possibility of guiding the sampling process by querying for certain types of samples. This paper addresses the problem of estimating the parameters of Bayesian networks in an active learning setting. We provide a theoretical framework for this problem, and an algorithm that chooses which active learning queries to generate based on the model learned so far. We present experimental results showing that our active learning algorithm can significantly reduce the need for training data in many situations.


Download Information

S. Tong and D. Koller (2001). "Active Learning for Parameter Estimation in Bayesian Networks." Conference on Advances in Neural Infomation Processing Systems (NIPS 2000). pdf ps.gz

Bibtex citation

@inproceedings{Tong+Koller:NIPS00,
  author =       "S. Tong and D. Koller",
  booktitle =    "Conference on Advances in Neural Infomation Processing
                 Systems (NIPS 2000)",
  title =        "Active Learning for Parameter Estimation in {B}ayesian
                 Networks",
  address =      "Denver, Colorado",
  year =         "2001",
}

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