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Graph classification and clustering based on vector space embedding /
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...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore ; London :
World Scientific,
©2010.
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| Colección: | Series in machine perception and artificial intelligence ;
v. 77. |
| Temas: | |
| Acceso en línea: | Texto completo |
| Sumario: | 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 related areas for a long time. |
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| Descripción Física: | 1 online resource (xiv, 331 pages) : illustrations |
| Bibliografía: | Includes bibliographical references (pages 309-328) and index. |
| ISBN: | 9789814304726 9814304727 1283144506 9781283144506 9786613144508 6613144509 |