Simple fast algorithms for the editing distance between trees and related problems
SIAM Journal on Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing the Edit-Distance between Unrooted Ordered Trees
ESA '98 Proceedings of the 6th Annual European Symposium on Algorithms
A Probabilistic Approach to Learning Costs for Graph Edit Distance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A survey on tree edit distance and related problems
Theoretical Computer Science
Learning probabilistic models of tree edit distance
Pattern Recognition
Learning Metrics Between Tree Structured Data: Application to Image Recognition
ECML '07 Proceedings of the 18th European conference on Machine Learning
SEDiL: Software for Edit Distance Learning
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Melody Recognition with Learned Edit Distances
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Robust web extraction: an approach based on a probabilistic tree-edit model
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
How far is it from here to there? a distance that is coherent with GP operators
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
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Trees provide a suited structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, or conversion of tree structured documents. In this context, many applications require the calculation of similarities between tree pairs. The most studied distance is likely the tree edit distance (ED) for which improvements in terms of complexity have been achieved during the last decade. However, this classic ED usually uses a priori fixed edit costs which are often difficult to tune, that leaves little room for tackling complex problems. In this paper, we focus on the learning of a stochastic tree ED. We use an adaptation of the Expectation-Maximization algorithm for learning the primitive edit costs. We carried out series of experiments that confirm the interest to learn a tree ED rather than a priori imposing edit costs.