03456nam 2200637 a 450 991045619740332120200520144314.01-283-14450-69786613144508981-4304-72-7(CKB)2490000000001897(EBL)731129(OCoLC)738433294(SSID)ssj0000522997(PQKBManifestationID)12233220(PQKBTitleCode)TC0000522997(PQKBWorkID)10539517(PQKB)11233363(MiAaPQ)EBC731129(WSP)00001066 (Au-PeEL)EBL731129(CaPaEBR)ebr10479748(CaONFJC)MIL314450(EXLCZ)99249000000000189720110712d2010 uy 0engur|n|---|||||txtccrGraph classification and clustering based on vector space embedding[electronic resource] /Kaspar Riesen & Horst BunkeSingapore ;Hackensack, N.J. World Scientific Pub. Co.20101 online resource (330 p.)Series in machine perception and artificial intelligence ;v. 77Description based upon print version of record.981-4304-71-9 Includes bibliographical references and index.Preface; 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 SpaceAppendix 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; IndexThis 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 rSeries in machine perception and artificial intelligence ;v. 77.Vector spacesCluster theory (Nuclear physics)Electronic books.Vector spaces.Cluster theory (Nuclear physics)006.42Riesen Kaspar989970Bunke Horst28587MiAaPQMiAaPQMiAaPQBOOK9910456197403321Graph classification and clustering based on vector space embedding2264278UNINA