Structural Pattern Recognition Using by Dwivedi Shri (17 results)

Condition: New. 1st edition NO-PA16APR2015-KAP.

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Condition: As New. Unread book in perfect condition.

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Condition: New. In English.

Hardback. Condition: New. 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). …

Hardcover. Condition: Brand New. 232 pages. 9.18x6.12x9.45 inches. In Stock.

Hardback. Condition: New. 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). …

Hardcover. Condition: new. Hardcover. 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) This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condition: new. Hardcover. 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) This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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) 250 pp. Englisch.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dr. Shri Prakash Dwivedi is an esteemed researcher and academic in the field of Computer Science and Engineering, specializing in pattern recognition, graph matching, and algorithms. He is currently serving as an Assistant Professor in the Departm.…

Hardcover. Condition: new. Hardcover. 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) This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…