LEADER 03456nam 2200637 a 450 001 9910456197403321 005 20200520144314.0 010 $a1-283-14450-6 010 $a9786613144508 010 $a981-4304-72-7 035 $a(CKB)2490000000001897 035 $a(EBL)731129 035 $a(OCoLC)738433294 035 $a(SSID)ssj0000522997 035 $a(PQKBManifestationID)12233220 035 $a(PQKBTitleCode)TC0000522997 035 $a(PQKBWorkID)10539517 035 $a(PQKB)11233363 035 $a(MiAaPQ)EBC731129 035 $a(WSP)00001066 035 $a(Au-PeEL)EBL731129 035 $a(CaPaEBR)ebr10479748 035 $a(CaONFJC)MIL314450 035 $a(EXLCZ)992490000000001897 100 $a20110712d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aGraph classification and clustering based on vector space embedding$b[electronic resource] /$fKaspar Riesen & Horst Bunke 210 $aSingapore ;$aHackensack, N.J. $cWorld Scientific Pub. Co.$d2010 215 $a1 online resource (330 p.) 225 1 $aSeries in machine perception and artificial intelligence ;$vv. 77 300 $aDescription based upon print version of record. 311 $a981-4304-71-9 320 $aIncludes bibliographical references and index. 327 $aPreface; Acknowledgments; Contents; 1. Introduction and Basic Concepts; 2. Graph Matching; 3. Graph Edit Distance; 4. Graph Data; 5. Kernel Methods; 6. Graph Embedding Using Dissimilarities; 7. Classification Experiments with Vector Space Embedded Graphs; 8. Clustering Experiments with Vector Space Embedded Graphs; 9. Conclusions; Appendix A Validation of Cost Parameters; Appendix B Visualization of Graph Data; Appendix C Classifier Combination; Appendix D Validation of a k-NN classifier in the Embedding Space; Appendix E Validation of a SVM classifier in the Embedding Space 327 $aAppendix F Validation of Lipschitz EmbeddingsAppendix G Validation of Feature Selection Algorithms and PCA Reduction; Appendix H Validation of Classifier Ensemble; Appendix I Validation of Kernel k-Means Clustering; Appendix J Confusion Matrices; Bibliography; Index 330 $aThis book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector. This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and r 410 0$aSeries in machine perception and artificial intelligence ;$vv. 77. 606 $aVector spaces 606 $aCluster theory (Nuclear physics) 608 $aElectronic books. 615 0$aVector spaces. 615 0$aCluster theory (Nuclear physics) 676 $a006.42 700 $aRiesen$b Kaspar$0989970 701 $aBunke$b Horst$028587 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910456197403321 996 $aGraph classification and clustering based on vector space embedding$92264278 997 $aUNINA