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Computing Distances between Evolutionary Trees

  • Bhaskar DasGupta
  • Xin He
  • Tao Jiang
  • Ming Li
  • John Tromp
  • Lusheng Wang
  • Louxin Zhang
Chapter

Abstract

Comparing objects to find their similarities or, equivalently, dissimilarities, is a fundamental issue in many fields including pattern recognition, image analysis, drug design, the study of thermodynamic costs of computing, cognitive science, etc. Various models have been introduced to measure the degree of similarity or dissimilarity in the literature. In the latter case the degree of dissimilarity is also often referred to as the distance. While some distances are straightforward to compute, e.g. the Hamming distance for binary strings, the Euclidean distance for geometric objects; some others are formulated as combinatorial optimization problems and thus pose nontrivial challenging algorithmic problems, sometimes even uncomputable, such as the universal information distance between two objects [4].

Keywords

Leaf Node Binary Tree Evolutionary Tree Internal Edge Weighted Tree 
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

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Bhaskar DasGupta
    • 1
  • Xin He
    • 2
  • Tao Jiang
    • 3
  • Ming Li
    • 4
  • John Tromp
    • 5
  • Lusheng Wang
    • 6
  • Louxin Zhang
    • 7
  1. 1.Rutgers UniversityUSA
  2. 2.SUNY at BuffaloUSA
  3. 3.McMaster UniversityCanada
  4. 4.City University of Hong Kong and University of WaterlooChina
  5. 5.CWIUSA
  6. 6.City University of Hong KongChina
  7. 7.National University of SingaporeSingapore

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