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Graph classification and clustering based on vector space embedding [[electronic resource] /] / Kaspar Riesen & Horst Bunke
| Graph classification and clustering based on vector space embedding [[electronic resource] /] / Kaspar Riesen & Horst Bunke |
| Autore | Riesen Kaspar |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, N.J., : World Scientific Pub. Co., 2010 |
| Descrizione fisica | 1 online resource (330 p.) |
| Disciplina | 006.42 |
| Altri autori (Persone) | BunkeHorst |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Vector spaces
Cluster theory (Nuclear physics) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-14450-6
9786613144508 981-4304-72-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910456197403321 |
Riesen Kaspar
|
||
| Singapore ; ; Hackensack, N.J., : World Scientific Pub. Co., 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Graph classification and clustering based on vector space embedding [[electronic resource] /] / Kaspar Riesen & Horst Bunke
| Graph classification and clustering based on vector space embedding [[electronic resource] /] / Kaspar Riesen & Horst Bunke |
| Autore | Riesen Kaspar |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, N.J., : World Scientific Pub. Co., 2010 |
| Descrizione fisica | 1 online resource (330 p.) |
| Disciplina | 006.42 |
| Altri autori (Persone) | BunkeHorst |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Vector spaces
Cluster theory (Nuclear physics) |
| ISBN |
1-283-14450-6
9786613144508 981-4304-72-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910780878603321 |
|
Riesen Kaspar
|
||
| Singapore ; ; Hackensack, N.J., : World Scientific Pub. Co., 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Graph classification and clustering based on vector space embedding / / Kaspar Riesen & Horst Bunke
| Graph classification and clustering based on vector space embedding / / Kaspar Riesen & Horst Bunke |
| Autore | Riesen Kaspar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, N.J., : World Scientific Pub. Co., 2010 |
| Descrizione fisica | 1 online resource (330 p.) |
| Disciplina | 006.42 |
| Altri autori (Persone) | BunkeHorst |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Vector spaces
Cluster theory (Nuclear physics) |
| ISBN |
1-283-14450-6
9786613144508 981-4304-72-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910809103003321 |
|
Riesen Kaspar
|
||
| Singapore ; ; Hackensack, N.J., : World Scientific Pub. Co., 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Structural Pattern Recognition with Graph Edit Distance : Approximation Algorithms and Applications / / by Kaspar Riesen
| Structural Pattern Recognition with Graph Edit Distance : Approximation Algorithms and Applications / / by Kaspar Riesen |
| Autore | Riesen Kaspar |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (164 p.) |
| Disciplina | 006.4 |
| Collana | Advances in Computer Vision and Pattern Recognition |
| Soggetto topico |
Pattern recognition
Data structures (Computer science) Pattern Recognition Data Structures |
| ISBN | 3-319-27252-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; 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
2.3.1 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 4.1 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 5.1.2 Nonlinear Support Vector Regression5.1.3 Predicting d λmin from dψ and d' ψ; 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 6.3 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 |
| Record Nr. | UNINA-9910298963203321 |
|
Riesen Kaspar
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||