大葉大學圖書館 |

Structural pattern recognition with graph edit distance[electronic resource] :approximation algorithms and applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.4
書名/作者:	Structural pattern recognition with graph edit distance : approximation algorithms and applications // by Kaspar Riesen.
作者:	Riesen, Kaspar.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xiii, 158 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Pattern recognition systems.
標題:	Computer vision.
標題:	Computer Science.
標題:	Pattern Recognition.
標題:	Data Structures.
ISBN:	9783319272528
ISBN:	9783319272511
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-27252-8

Structural pattern recognition with graph edit distance[electronic resource] :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:

189561
Pattern recognition systems.

LC Class. No.: TK7882.P3

Dewey Class. No.: 006.4

Structural pattern recognition with graph edit distance[electronic resource] :approximation algorithms and applications /
LDR:02983nam a2200325 a 4500 001 444754
003 DE-He213
005 20160524110128.0
006 m d
007 cr nn 008maaau
008 160715s2015 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 489819
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 : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
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 189561
650 0 $a Computer vision. $3 403529
650 1 4 $a Computer Science. $3 423143
650 2 4 $a Pattern Recognition. $3 463916
650 2 4 $a Data Structures. $3 467911
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a Advances in computer vision and pattern recognition. $3 466956
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-27252-8
950 $a Computer Science (Springer-11645)