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Learning Structural Variations in Shock Trees

  • Andrea Torsello
  • Edwin R. Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

In this paper we investigate how to construct a shape space for sets of shock trees. To do this we construct a super-tree to span the union of the set of shock trees. We learn this super-tree and the correspondences of the node in the sample trees using a maximizing likelihood approach. We show that the likelihood is maximized by the set of correspondences that minimizes the sum of the tree edit distance between pair of trees, subject to edge consistency constraints. Each node of the super-tree corresponds to a dimension of the pattern space. Individual such trees are mapped to vectors in this pattern space.

Keywords

Multiple Path Shock Tree Shape Space Edit Operation Pattern Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Andrea Torsello
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkHeslingtonUK

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