9781032850344 - structural pattern recognition using graph matching: approximate and error-tolerant algorithms di dwivedi, shri prakash; shankar singh, ravi (20 risultati)
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Da: UK BOOKS STORE, London, Regno UnitoUK BOOKS STORE
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Condizione: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if t…he Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 232 pages. 9.18x6.12x9.45 inches. In Stock.
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Hardcover. Condizione: 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 algori…thms 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.
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Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
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EUR 157,83
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Hardcover. Condizione: 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 algori…thms 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.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 180,00
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Buch. Condizione: 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 similarit…y 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.
Structural Pattern Recognition using Graph Matching
Shri Prakash Dwivedi (G. B. Pant University of Agriculture & Technology, India)|Ravi Shankar Singh
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Da: moluna, Greven, Germaniamoluna
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Condizione: 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 Ass…istant Professor in the Departm.
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Da: PBShop.store US, Wood Dale, U.S.A.PBShop.store US
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HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Da: PBShop.store UK, Fairford, Regno UnitoPBShop.store UK
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HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 mea…sures, 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).
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Da: AussieBookSeller, Truganina, AustraliaAussieBookSeller
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EUR 319,06
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Hardcover. Condizione: 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 algori…thms 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.
