Structural Pattern Recognition with Graph Edit Distance

Approximation Algorithms and Applications

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Programming, Data Modeling & Design, General Computing
Cover of the book Structural Pattern Recognition with Graph Edit Distance by Kaspar Riesen, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Kaspar Riesen ISBN: 9783319272528
Publisher: Springer International Publishing Publication: January 9, 2016
Imprint: Springer Language: English
Author: Kaspar Riesen
ISBN: 9783319272528
Publisher: Springer International Publishing
Publication: January 9, 2016
Imprint: Springer
Language: English

This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED). The book also provides a detailed review of a diverse selection of novel methods related to GED, and concludes by suggesting possible avenues for future research. Topics and features: formally introduces the concept of GED, and highlights the basic properties of this graph matching paradigm; describes a reformulation of GED to a quadratic assignment problem; illustrates how the quadratic assignment problem of GED can be reduced to a linear sum assignment problem; reviews strategies for reducing both the overestimation of the true edit distance and the matching time in the approximation framework; examines the improvement demonstrated by the described algorithmic framework with respect to the distance accuracy and the matching time; includes appendices listing the datasets employed for the experimental evaluations discussed in the book.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED). The book also provides a detailed review of a diverse selection of novel methods related to GED, and concludes by suggesting possible avenues for future research. Topics and features: formally introduces the concept of GED, and highlights the basic properties of this graph matching paradigm; describes a reformulation of GED to a quadratic assignment problem; illustrates how the quadratic assignment problem of GED can be reduced to a linear sum assignment problem; reviews strategies for reducing both the overestimation of the true edit distance and the matching time in the approximation framework; examines the improvement demonstrated by the described algorithmic framework with respect to the distance accuracy and the matching time; includes appendices listing the datasets employed for the experimental evaluations discussed in the book.

More books from Springer International Publishing

Cover of the book 100 Chemical Myths by Kaspar Riesen
Cover of the book The Repressed Memory Epidemic by Kaspar Riesen
Cover of the book Kalevi Holsti: Major Texts on War, the State, Peace, and International Order by Kaspar Riesen
Cover of the book Big Data, Cloud Computing, Data Science & Engineering by Kaspar Riesen
Cover of the book Machine Learning in Medicine - Cookbook by Kaspar Riesen
Cover of the book Diaspora as Cultures of Cooperation by Kaspar Riesen
Cover of the book On the Penitentiary System in the United States and its Application to France by Kaspar Riesen
Cover of the book Western Balkan Economies in Transition by Kaspar Riesen
Cover of the book Performance Analysis of Computer Networks by Kaspar Riesen
Cover of the book Work and Family Interface in the International Career Context by Kaspar Riesen
Cover of the book Metal Nanoparticles and Clusters by Kaspar Riesen
Cover of the book Endogenous ADP-Ribosylation by Kaspar Riesen
Cover of the book GTPases by Kaspar Riesen
Cover of the book Shell and Membrane Theories in Mechanics and Biology by Kaspar Riesen
Cover of the book An Introduction to Fuzzy Linear Programming Problems by Kaspar Riesen
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy