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Bridging the gap between graph edit distance and kernel machines /
In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain - commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for in...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore ; Hackensack, NJ :
World Scientific Pub.,
©2007.
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| Colección: | Series in machine perception and artificial intelligence ;
v. 68. |
| Temas: | |
| Acceso en línea: | Texto completo |
| Sumario: | In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain - commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching. This book focuses on graph kernel functions that are highly tolerant towards structura. |
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| Descripción Física: | 1 online resource (xi, 232 pages) : illustrations |
| Bibliografía: | Includes bibliographical references (pages 221-230) and index. |
| ISBN: | 9789812770202 9812770208 |