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[null,"1.","Record Nr.",null,"UNINA9910456197403321"]
[null,null,"Autore",null,"Riesen Kaspar"]
[null,null,"Titolo",null,"Graph classification and clustering based on vector space embedding [[electronic resource] /] / Kaspar Riesen & Horst Bunke"]
[null,null,"Pubbl/distr/stampa",null,"Singapore ; ; Hackensack, N.J., : World Scientific Pub. Co., 2010"]
[null,null,"ISBN",null,"1-283-14450-6"]
[{"$nulls":4},"9786613144508"]
[{"$nulls":4},"981-4304-72-7"]
[null,null,"Descrizione fisica",null,"1 online resource (330 p.)"]
[null,null,"Collana",null,"Series in machine perception and artificial intelligence ; ; v. 77"]
[null,null,"Disciplina",null,"006.42"]
[null,null,"Soggetti",null,"Vector spaces"]
[{"$nulls":4},"Cluster theory (Nuclear physics)"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"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 Space"]
[{"$nulls":4},"Appendix 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"]
[null,null,null,"Sommario/riassunto","This 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"]