Computing Distances between Evolutionary Trees View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1998

AUTHORS

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

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]. More... »

PAGES

781-822

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4613-0303-9_11

DOI

http://dx.doi.org/10.1007/978-1-4613-0303-9_11

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004819157


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