Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 110,68
Anzahl: 1 verfügbar
In den WarenkorbZustand: New.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 151,42
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In den WarenkorbHardcover. Zustand: Brand New. 264 pages. 9.25x6.10x0.79 inches. In Stock.
Sprache: Englisch
Verlag: Springer Nature Singapore, Springer Nature Singapore Mai 2019, 2019
ISBN 10: 981137421X ISBN 13: 9789811374210
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Buch. Zustand: Neu. Neuware -This book presents the application of a comparatively simple nonparametric regression algorithm, known as the multivariate adaptive regression splines (MARS) surrogate model, which can be used to approximate the relationship between the inputs and outputs, and express that relationship mathematically. The book first describes the MARS algorithm, then highlights a number of geotechnical applications with multivariate big data sets to explore the approach¿s generalization capabilities and accuracy. As such, it offers a valuable resource for all geotechnical researchers, engineers, and general readers interested in big data analysis.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 264 pp. Englisch.
Sprache: Englisch
Verlag: Springer Nature Singapore, Springer Nature Singapore, 2019
ISBN 10: 981137421X ISBN 13: 9789811374210
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents the application of a comparatively simple nonparametric regression algorithm, known as the multivariate adaptive regression splines (MARS) surrogate model, which can be used to approximate the relationship between the inputs and outputs, and express that relationship mathematically. The book first describes the MARS algorithm, then highlights a number of geotechnical applications with multivariate big data sets to explore the approach's generalization capabilities and accuracy. As such, it offers a valuable resource for all geotechnical researchers, engineers, and general readers interested in big data analysis.