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ICDM

2007

IEEE

2007

IEEE

Tree edit distance is one of the most frequently used distance measures for comparing trees. When using the tree edit distance, we need to determine the cost of each operation, but this is a labor-intensive and highly skilled task. This paper proposes an algorithm for learning the costs of tree edit operations from training data consisting of pairs of similar trees. To formalize the cost learning problem, we deﬁne a probabilistic model for tree alignment that is a variant of tree edit distance. Then, the parameters of the model are estimated using the expectation maximization (EM) technique. In this paper, we develop an algorithm for parameter learning that is polynomial in time (O(mn2 d6 )) and space (O(n2 d4 )) where n, d, and m represent the size of the trees, the maximum degree of trees, and the number of training pairs of trees, respectively.

Related Content

Added |
03 Jun 2010 |

Updated |
03 Jun 2010 |

Type |
Conference |

Year |
2007 |

Where |
ICDM |

Authors |
Atsuhiro Takasu, Daiji Fukagawa, Tatsuya Akutsu |

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