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Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloHardcover. Condizione: Brand New. 264 pages. 9.25x6.10x0.79 inches. In Stock.
Lingua: Inglese
Editore: Springer Nature Singapore, Springer Nature Singapore, 2019
ISBN 10: 981137421X ISBN 13: 9789811374210
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 111,53
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Aggiungi al carrelloBuch. Condizione: 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.
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Aggiungi al carrellopaperback. Condizione: New. Paperback. Pub Date: 2019-01-01 Pages: 240 Language: English Publisher: Science Press Many geotechnical problems rely on empirical methods to design equations or expressions chart to determine the response of the system to input variables. MARS Applications in Geotechnical En .
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 169,46
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EUR 86,24
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer Nature Singapore Mai 2019, 2019
ISBN 10: 981137421X ISBN 13: 9789811374210
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 106,99
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 264 pp. Englisch.
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents the nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) and its applications Introduces simple algorithms that are easy to interpret and deliver good computational efficien.
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Da: preigu, Osnabrück, Germania
EUR 95,70
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Aggiungi al carrelloBuch. Condizione: Neu. MARS Applications in Geotechnical Engineering Systems | Multi-Dimension with Big Data | Wengang Zhang | Buch | xxi | Englisch | 2019 | Springer | EAN 9789811374210 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
Lingua: Inglese
Editore: Springer, Springer Mai 2019, 2019
ISBN 10: 981137421X ISBN 13: 9789811374210
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 106,99
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 264 pp. Englisch.