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SODA

2001

ACM

2001

ACM

Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a joint probability distribution. Once one has chosen to use a Markov network model, one aims to choose the model that "best explains" the data that has been observed--this model can then be used to make predictions about future data. We show that the problem of learning a maximum likelihood Markov network given certain observed data can be reduced to the problem of identifying a maximum weight low-treewidth graph under a given input weight function. We give the first constant factor approximation algorithm for this problem. More precisely, for any fixed treewidth objective k, we find a treewidth-k graph with an f(k) fraction of the maximum possible weight of any treewidthk graph.

Related Content

Added |
31 Oct 2010 |

Updated |
31 Oct 2010 |

Type |
Conference |

Year |
2001 |

Where |
SODA |

Authors |
David R. Karger, Nathan Srebro |

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