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ICPR

2004

IEEE

2004

IEEE

Graph edit distance provides an error-tolerant way to measure distances between attributed graphs. The effectiveness of edit distance based graph classification algorithms relies on the adequate definition of edit operation costs. We propose a cost inference method that is based on a distribution estimation of edit operations. For this purpose we employ an Expectation Maximization algorithm to learn mixture densities from a labeled sample of graphs and derive edit costs that are subsequently applied in the context of a graph edit distance computation framework. We evaluate the performance of the proposed distance model in comparison to another recently introduced learning model for edit costs.

Related Content

Added |
09 Nov 2009 |

Updated |
09 Nov 2009 |

Type |
Conference |

Year |
2004 |

Where |
ICPR |

Authors |
Horst Bunke, Michel Neuhaus |

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