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The problem of learning metrics between structured data (strings, trees or graphs) has been the subject of various recent papers. With regard to the specific case of trees, some approaches focused on the learning of edit probabilities required to compute a so-called stochas-tic tree edit distance. However, to reduce the algorithmic and learning constraints,(More)
A c c e p t e d m a n u s c r i p t Abstract Nowadays, there is a growing interest in machine learning and pattern recognition for tree-structured data. Trees actually provide a suitable structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, computer music, or conversion of(More)
In this paper, we present our global image processing frameworkof interventional planning and assistance for ascending aorta dissections. The preoperative stage of that framework performs the extraction of aortic dissection features in Computed Tomography Angiography (CTA) images. It mainly consists ofa customized fast marching segmentation. The(More)
Learning the parameters of the edit distance has been increasingly studied during the past few years to improve the assessment of similarities between struc-tured data, such as strings, trees or graphs. Often based on the optimization of the likelihood of pairs of data, the learned models usually take the form of prob-abilistic state machines, such as(More)
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