Kaspar Riesen 
Structural Pattern Recognition with Graph Edit Distance [PDF ebook] 
Approximation Algorithms and Applications

Sokongan

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 discussedin the book.

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Part I: Foundations and Applications of Graph Edit Distance .- Introduction and Basic Concepts.- Graph Edit Distance.- Bipartite Graph Edit Distance.-  Part II: Recent Developments and Research on Graph Edit Distance .- Improving the Distance Accuracy of Bipartite Graph Edit Distance.- Learning Exact Graph Edit Distance.- Speeding Up Bipartite Graph Edit Distance.- Conclusions and Future Work.- Appendix A: Experimental Evaluation of Sorted Beam Search.- Appendix B: Data Sets.

Mengenai Pengarang

Dr. Kaspar Riesen is a university lecturer of computer science in the Institute for Information Systems at the University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland.

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Bahasa Inggeris ● Format PDF ● Halaman-halaman 158 ● ISBN 9783319272528 ● Saiz fail 3.3 MB ● Penerbit Springer International Publishing ● Bandar raya Cham ● Negara CH ● Diterbitkan 2016 ● Muat turun 24 bulan ● Mata wang EUR ● ID 4822607 ● Salin perlindungan Social DRM

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