9789819901876 - hypergraph computation (artificial intelligence: foundations, theory, and algorithms) von dai, qionghai; gao, yue (6 Ergebnisse)
Sprache: Englisch
Verlag: Rawat Publications, 2023
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Serie: Buch 15 von 15 - Artificial Intelligence: Foundations, Theory, and Algorithms
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Serie: Buch 15 von 15 - Artificial Intelligence: Foundations, Theory, and Algorithms
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Paperback. Zustand: Brand New. 260 pages. 9.25x6.10x0.55 inches. In Stock.
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Sprache: Englisch
Verlag: Springer, Springer, 2023
Serie: Buch 15 von 15 - Artificial Intelligence: Foundations, Theory, and Algorithms
- Softcover
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Taschenbuch. Zustand: 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, et…c. 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.
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Sprache: Englisch
Verlag: Springer, 2023
Serie: Buch 15 von 15 - Artificial Intelligence: Foundations, Theory, and Algorithms
- Softcover
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Taschenbuch. Zustand: Neu. Hypergraph Computation | Qionghai Dai (u. a.) | Taschenbuch | Artificial Intelligence: Foundations, Theory, and Algorithms | xvi | Englisch | 2023 | Springer | EAN 9789819901876 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]sprin…ger[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer Nature Singapore, 2023
Serie: Buch 15 von 15 - Artificial Intelligence: Foundations, Theory, and Algorithms
- Softcover
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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 260 | Sprache: Englisch | Produktart: Bücher | 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 relational learning tasks. 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 learning tasks. 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.
