株式会社極東書店トップ ＞ 商品一覧 ＞ Structural Pattern Recognition using Graph Matching: Approximate and Error-Tolerant Algorithms.
商品詳細
Structural Pattern Recognition using Graph Matching: Approximate and Error-Tolerant Algorithms.
・ISBN 978-1-032-85034-4 hard GB£ 171.99
￥54,486.- (税込) ※（※）価格はご注文時の参考価格となります。
納品価格につきましては書籍の入荷時点で確定となります。
版元の原価改定、外国為替の変動等により異なる場合がございますので、予めご了承下さい。
| 著者・編者 | Dwivedi, Shri Prakash / Shankar Singh, Ravi, |
|---|---|
| 出版社 | (Chapman & Hall/CRC, UK) |
| 出版年月 | 2025 |
| ページ数 | 230 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A04-8647＞ |
解説
This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. It explains graph matching techniques, unearthing the core principles of graph similarity measures, subgraph isomorphism, and advanced algorithms tailored to various pattern recognition tasks. It bridges the gap between theory and application by providing case studies, hands-on examples, and applications. It is a reference book for academicians, researchers, and students working in the fields of structural pattern recognition, computer vision, artificial intelligence, and data science.
* Begins with the fundamentals of graph theory, graph matching algorithms, and structural pattern recognition concepts and explains the principles, methodologies, and practical implementations
* Presents relevant case studies and hands-on examples across chapters to guide making informed decisions by graph matching
* Discusses various graph-matching algorithms, including exact and approximate methods, geometric methods, spectral techniques, graph kernels, and graph neural networks, including practical examples to illustrate the strengths and limitations of each approach
* Showcases the versatility of graph matching in real-world applications, such as image analysis, biological molecule identification, object recognition, social network clustering, and recommendation systems
* Describes deep learning models for graph matching, including graph convolutional networks (GCNs) and graph neural networks (GNNs)