國立虎尾科技大學 |

Structural pattern recognition with graph edit distance = approximation algorithms and applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Structural pattern recognition with graph edit distance/ by Kaspar Riesen.
Reminder of title:	approximation algorithms and applications /
Author:	Riesen, Kaspar.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	xiii, 158 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Pattern recognition systems. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-27252-8
ISBN:	9783319272528

Structural pattern recognition with graph edit distance = approximation algorithms and applications /
Riesen, Kaspar.

Structural pattern recognition with graph edit distanceapproximation algorithms and applications /[electronic resource] :by Kaspar Riesen. - Cham :Springer International Publishing :2015. - xiii, 158 p. :ill., digital ;24 cm. - Advances in computer vision and pattern recognition,2191-6586. - Advances in computer vision and pattern recognition..

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.

This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED), one of the most flexible graph distance models available. 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 Researchers and graduate students interested in the field of structural pattern recognition will find this focused work to be an essential reference on the latest developments in GED. 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.

ISBN: 9783319272528

Standard No.: 10.1007/978-3-319-27252-8doiSubjects--Topical Terms:

557384
Pattern recognition systems.

LC Class. No.: TK7882.P3

Dewey Class. No.: 006.4

Structural pattern recognition with graph edit distance = approximation algorithms and applications /
LDR:02983nam a2200325 a 4500 001 839097
003 DE-He213
005 20160524110128.0
006 m d
007 cr nn 008maaau
008 160616s2015 gw s 0 eng d
020 $a 9783319272528 $q (electronic bk.)
020 $a 9783319272511 $q (paper)
024 7 $a 10.1007/978-3-319-27252-8 $2 doi
035 $a 978-3-319-27252-8
040 $a GP $c GP
041 0 $a eng
050 4 $a TK7882.P3
072 7 $a UYQP $2 bicssc
072 7 $a COM016000 $2 bisacsh
082 0 4 $a 006.4 $2 23
090 $a TK7882.P3 $b R561 2015
100 1 $a Riesen, Kaspar. $3 856884
245 1 0 $a Structural pattern recognition with graph edit distance $h [electronic resource] : $b approximation algorithms and applications / $c by Kaspar Riesen.
260 $a Cham : $c 2015. $b Springer International Publishing : $b Imprint: Springer,
300 $a xiii, 158 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in computer vision and pattern recognition, $x 2191-6586
505 0 $a 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.
520 $a This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED), one of the most flexible graph distance models available. 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 Researchers and graduate students interested in the field of structural pattern recognition will find this focused work to be an essential reference on the latest developments in GED. 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.
650 0 $a Pattern recognition systems. $3 557384
650 0 $a Computer vision. $3 561800
650 1 4 $a Computer Science. $3 593922
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Data Structures. $3 669824
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Advances in computer vision and pattern recognition. $3 886855
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-27252-8
950 $a Computer Science (Springer-11645)