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In den WarenkorbZustand: Hervorragend. Zustand: Hervorragend | Seiten: 168 | Sprache: Englisch | Produktart: Bücher.
Verlag: Springer Nature Switzerland, Springer International Publishing Sep 2024, 2024
ISBN 10: 3031423151 ISBN 13: 9783031423154
Sprache: Englisch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
EUR 42,79
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In den WarenkorbTaschenbuch. Zustand: Neu. Neuware -This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of big data. The book begins by providing background on dynamic graph learning. The authors discuss why it has attracted considerable research attention in recent years and has become well recognized as an advanced technique. After covering the key topics related to dynamic graph learning, the book discusses the recent advancements in the area. The authors then explain how these techniques can be practically applied for several purposes, including feature selection, feature projection, and data clustering.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 168 pp. Englisch.
Verlag: Springer Nature Switzerland, 2024
ISBN 10: 3031423151 ISBN 13: 9783031423154
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 42,79
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbTaschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of big data. The book begins by providing background on dynamic graph learning. The authors discuss why it has attracted considerable research attention in recent years and has become well recognized as an advanced technique. After covering the key topics related to dynamic graph learning, the book discusses the recent advancements in the area. The authors then explain how these techniques can be practically applied for several purposes, including feature selection, feature projection, and data clustering.