Uncertainty in Artificial Intelligence
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'Say EM' for Selecting Probabilistic Models for Logical Sequences
Kristian Kersting, Tapani Raiko
Abstract:
Many real world sequences such as protein secondary structures or shell logs exhibit a rich internal structures. Traditional probabilistic models of sequences, however, consider sequences of flat symbols only. Logical hidden Markov models have been proposed as one solution. They deal with logical sequences, i.e., sequences over an alphabet of logical atoms. This comes at the expense of a more complex model selection problem. Indeed, different abstraction levels have to be explored. In this paper, we propose a novel method for selecting logical hidden Markov models from data called SAGEM. SAGEM combines generalized expectation maximization, which optimizes parameters, with structure search for model selection using inductive logic programming refinement operators. We provide convergence and experimental results that show SAGEM's effectiveness.
Keywords:
Pages: 300-307
PS Link:
PDF Link: /papers/05/p300-kersting.pdf
BibTex:
@INPROCEEDINGS{Kersting05,
AUTHOR = "Kristian Kersting and Tapani Raiko",
TITLE = "'Say EM' for Selecting Probabilistic Models for Logical Sequences",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "300--307"
}


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