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