Graph-powered machine learning

1. Machine learning and graphs : an introduction -- 2. Graph data engineering -- 3. Graphs in machine learning applications -- 4. Content-based recommendations -- 5. Collaborative filtering -- 6. Session-based recommendations -- 7. Context-aware and hybrid recommendations -- 8. Basic approaches to g...

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1. Verfasser:	Negro, Alessandro (VerfasserIn)
Weitere Verfasser:	Webber, Jim (VerfasserIn eines Vorworts)
Format:	UnknownFormat
Sprache:	eng
Veröffentlicht:	Shelter Island Manning 2021
Schlagworte:	Machine learning > Graph theory > Graphic methods > Machine learning ; Graphic methods > Maschinelles Lernen > Graphentheorie > Cluster-Analyse
Online Zugang:	Inhaltsverzeichnis
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Zusammenfassung:	1. Machine learning and graphs : an introduction -- 2. Graph data engineering -- 3. Graphs in machine learning applications -- 4. Content-based recommendations -- 5. Collaborative filtering -- 6. Session-based recommendations -- 7. Context-aware and hybrid recommendations -- 8. Basic approaches to graph-powered fraud detection -- 9. Proximity-based algorithms -- 10. Social metwork analysis against fraud -- 11. Graph-based natural language processing -- 12. Knowledge graphs. Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendiation systems. "Graph-powered machine learning" teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you'll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks
Beschreibung:	Literaturangaben
Beschreibung:	xxiv, 467 Seiten Illustrationen, Diagramme 24 cm
ISBN:	9781617295645 978-1-61729-564-5 1617295647 1-61729-564-7