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Hypergraph Computation (Artificial Intelligence: Foundations, Theory, and Algorithms) - Couverture rigide
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- ÉditeurSpringer
- Date d'édition2023
- ISBN 10 9819901847
- ISBN 13 9789819901845
- ReliureTapa dura
- Numéro d'édition1
- Nombre de pages262
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Hypergraph Computation (Artificial Intelligence: Foundations, Theory, and Algorithms) by Dai, Qionghai, Gao, Yue [Hardcover ]
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(2023)
ISBN 10 : 9819901847
ISBN 13 : 9789819901845
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Description du livre Hardcover. Etat : new. This item is printed on demand. N° de réf. du vendeur 9789819901845
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Hypergraph Computation (Artificial Intelligence: Foundations, Theory, and Algorithms)
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(2023)
ISBN 10 : 9819901847
ISBN 13 : 9789819901845
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Description du livre Etat : New. 1st ed. 2023 edition. N° de réf. du vendeur 26396348988
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Hypergraph Computation
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(2023)
ISBN 10 : 9819901847
ISBN 13 : 9789819901845
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Description du livre Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. N° de réf. du vendeur ABEOCT23-24817
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Hypergraph Computation (Artificial Intelligence: Foundations, Theory, and Algorithms)
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(2023)
ISBN 10 : 9819901847
ISBN 13 : 9789819901845
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Description du livre Etat : New. N° de réf. du vendeur 401109475
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Hypergraph Computation
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Springer Nature Singapore Mai 2023
(2023)
ISBN 10 : 9819901847
ISBN 13 : 9789819901845
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Description du livre Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This open access book discusses the theory and methods of hypergraph computation. Many underlying relationships among data can be represented using graphs, for example in the areas including computer vision, molecular chemistry, molecular biology, etc. In the last decade, methods like graph-based learning and neural network methods have been developed to process such data, they are particularly suitable for handling relationallearningtasks. In many real-world problems, however, relationships among the objects of our interest are more complex than pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for learningtasks.Hypergraph, as a generation of graph, has shown superior performance on modelling complex correlations compared with graph. Recent years have witnessed a great popularity of researches on hypergraph-related AI methods, which have been used in computer vision, social media analysis,etc. We summarize these attempts as a new computing paradigm, called hypergraph computation, which is to formulate the high-order correlations underneath the data using hypergraph, and then conduct semantic computing on the hypergraph for different applications. The content of this book consists of hypergraph computation paradigms, hypergraph modelling, hypergraph structure evolution, hypergraph neural networks, and applications of hypergraph computation in different fields. We further summarize recent achievements and future directions on hypergraph computation in this book. 260 pp. Englisch. N° de réf. du vendeur 9789819901845
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Hypergraph Computation
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Description du livre Etat : New. N° de réf. du vendeur 46003592-n
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Hypergraph Computation (Artificial Intelligence: Foundations, Theory, and Algorithms)
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ISBN 13 : 9789819901845
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Description du livre Etat : New. N° de réf. du vendeur I-9789819901845
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Hypergraph Computation
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Description du livre Etat : New. N° de réf. du vendeur 46003592-n
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Hypergraph Computation
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Springer Nature Singapore
(2023)
ISBN 10 : 9819901847
ISBN 13 : 9789819901845
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Description du livre Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book discusses the theory and methods of hypergraph computation. Many underlying relationships among data can be represented using graphs, for example in the areas including computer vision, molecular chemistry, molecular biology, etc. In the last decade, methods like graph-based learning and neural network methods have been developed to process such data, they are particularly suitable for handling relationallearningtasks. In many real-world problems, however, relationships among the objects of our interest are more complexthan pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for learningtasks.Hypergraph, as a generation of graph, has shown superior performance on modelling complex correlations compared with graph. Recent years have witnessed a great popularity of researches on hypergraph-related AI methods, which have been used in computer vision, social media analysis,etc. We summarize these attempts as a new computing paradigm, called hypergraph computation, which is to formulate thehigh-order correlations underneath the data using hypergraph, and then conduct semantic computing on the hypergraph for different applications. The content of this book consists of hypergraph computation paradigms, hypergraph modelling, hypergraph structure evolution, hypergraph neural networks, and applications of hypergraph computation in different fields. We further summarize recent achievements and future directions on hypergraph computation in this book. N° de réf. du vendeur 9789819901845
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Hypergraph Computation
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ISBN 13 : 9789819901845
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Description du livre Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The first comprehensive and systematic overview for hypergraph computationRich blend of basic knowledge, theoretical analysis, algorithm introduction, and key applicationsDescribes hypergraph computation applications in computer vision, med. N° de réf. du vendeur 790280503
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