ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Learning probabilistic models of tree edit distance
Pattern Recognition
Optimizing textual entailment recognition using particle swarm optimization
TextInfer '09 Proceedings of the 2009 Workshop on Applied Textual Inference
Automatic learning of edit costs based on interactive and adaptive graph recognit
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Construction of model of structured documents based on machine learning
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Automatic human action recognition in videos by graph embedding
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Learning stochastic tree edit distance
ECML'06 Proceedings of the 17th European conference on Machine Learning
A graph matching based approach to fingerprint classification using directional variance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Graph matching – challenges and potential solutions
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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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.