LEADER 06465nam 22006615 450 001 9910298963203321 005 20251113183618.0 010 $a3-319-27252-7 024 7 $a10.1007/978-3-319-27252-8 035 $a(CKB)3710000000571760 035 $a(EBL)4332317 035 $a(SSID)ssj0001607096 035 $a(PQKBManifestationID)16314684 035 $a(PQKBTitleCode)TC0001607096 035 $a(PQKBWorkID)14897049 035 $a(PQKB)10043751 035 $a(DE-He213)978-3-319-27252-8 035 $a(MiAaPQ)EBC4332317 035 $a(PPN)191705942 035 $a(EXLCZ)993710000000571760 100 $a20160109d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStructural Pattern Recognition with Graph Edit Distance $eApproximation Algorithms and Applications /$fby Kaspar Riesen 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (164 p.) 225 1 $aAdvances in Computer Vision and Pattern Recognition,$x2191-6594 300 $aDescription based upon print version of record. 311 08$a3-319-27251-9 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aPreface; Acknowledgments; Contents; Part I Foundations and Applicationsof Graph Edit Distance; 1 Introduction and Basic Concepts; 1.1 Pattern Recognition; 1.1.1 Statistical and Structural Pattern Recognition; 1.2 Graph and Subgraph; 1.3 Graph Matching; 1.3.1 Exact Graph Matching; 1.3.2 Error-Tolerant Graph Matching; 1.4 Outline of the Book; References; 2 Graph Edit Distance; 2.1 Basic Definition and Properties; 2.1.1 Conditions on Edit Cost Functions; 2.1.2 Example Definitions of Cost Functions; 2.2 Computation of Exact Graph Edit Distance; 2.3 Graph Edit Distance-Based Pattern Recognition 327 $a2.3.1 Nearest-Neighbor Classification2.3.2 Kernel-Based Classification; 2.3.3 Classification of Vector Space Embedded Graphs; References; 3 Bipartite Graph Edit Distance; 3.1 Graph Edit Distance as Quadratic Assignment Problem; 3.2 Bipartite Graph Edit Distance; 3.2.1 Deriving Upper and Lower Bounds on the Graph Edit Distance; 3.2.2 Summary; 3.3 Experimental Evaluation; 3.4 Pattern Recognition Applications of Bipartite Graph Edit Distance; References; Part II Recent Developments and Researchon Graph Edit Distance; 4 Improving the Distance Accuracy of Bipartite Graph Edit Distance 327 $a4.1 Change of Notation4.2 Improvements via Search Strategies; 4.2.1 Iterative Search; 4.2.2 Floating Search; 4.2.3 Genetic Search; 4.2.4 Greedy Search; 4.2.5 Genetic Search with Swap Strategy; 4.2.6 Beam Search; 4.2.7 Experimental Evaluation; 4.3 Improvements via Integration of Node Centrality Information; 4.3.1 Node Centrality Measures; 4.3.2 Integrating Node Centrality in the Assignment; 4.3.3 Experimental Evaluation; References; 5 Learning Exact Graph Edit Distance; 5.1 Predicting Exact Graph Edit Distance from Lower and Upper Bounds; 5.1.1 Linear Support Vector Regression 327 $a5.1.2 Nonlinear Support Vector Regression5.1.3 Predicting d ?min from d? and d' ?; 5.1.4 Experimental Evaluation; 5.2 Predicting the Correctness of Node Edit Operations; 5.2.1 Features for Node Edit Operations; 5.2.2 Experimental Evaluation; References; 6 Speeding Up Bipartite Graph Edit Distance; 6.1 Suboptimal Assignment Algorithms; 6.1.1 Basic Greedy Assignment; 6.1.2 Tie Break Strategy; 6.1.3 Refined Greedy Assignment; 6.1.4 Greedy Assignment Regarding Loss; 6.1.5 Order of Node Processing; 6.1.6 Greedy Sort Assignment; 6.2 Relations to Exact Graph Edit Distance 327 $a6.3 Experimental EvaluationReferences; 7 Conclusions and Future Work; 7.1 Main Contributions and Findings; 7.2 Future Work; References; 8 Appendix A: Experimental Evaluation of Sorted Beam Search; 8.1 Sorting Criteria; 8.1.1 Confident; 8.1.2 Unique; 8.1.3 Divergent; 8.1.4 Leader; 8.1.5 Interval; 8.1.6 Deviation; 8.2 Experimental Evaluation; References; 9 Appendix B: Data Sets; 9.1 LETTER Graphs; 9.2 GREC Graphs; 9.3 FP Graphs; 9.4 AIDS Graphs; 9.5 MUTA Graphs; 9.6 PROT Graphs; 9.7 GREYC Graphs; References; Index 330 $aThis unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED), one of the most flexible graph distance models available. The book also provides a detailed review of a diverse selection of novel methods related to GED, and concludes by suggesting possible avenues for future research. Topics and features: Formally introduces the concept of GED, and highlights the basic properties of this graph matching paradigm Describes a reformulation of GED to a quadratic assignment problem Illustrates how the quadratic assignment problem of GED can be reduced to a linear sum assignment problem Reviews strategies for reducing both the overestimation of the true edit distance and the matching time in the approximation framework Examines the improvement demonstrated by the described algorithmic framework with respect to the distance accuracy and the matching time Includes appendices listing the datasets employed for the experimental evaluations discussed in the book Researchers and graduate students interested in the field of structural pattern recognition will find this focused work to be an essential reference on the latest developments in GED. Dr. Kaspar Riesen is a university lecturer of computer science in the Institute for Information Systems at the University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland. 410 0$aAdvances in Computer Vision and Pattern Recognition,$x2191-6594 606 $aPattern recognition systems 606 $aArtificial intelligence$xData processing 606 $aAutomated Pattern Recognition 606 $aData Science 615 0$aPattern recognition systems. 615 0$aArtificial intelligence$xData processing. 615 14$aAutomated Pattern Recognition. 615 24$aData Science. 676 $a006.4 700 $aRiesen$b Kaspar$4aut$4http://id.loc.gov/vocabulary/relators/aut$0989970 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910298963203321 996 $aStructural Pattern Recognition with Graph Edit Distance$92507055 997 $aUNINA