MARS applications in geotechnical engineering systems : multi-dimension with big data /

MARS applications in geotechnical engineering systems : multi-dimension with big data /

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Bibliographic Details
Author / Creator:	Zhang, Wen'gang, author.
Imprint:	Singapore : Springer, [2020] ©2020
Description:	1 online resource.
Language:	English
Subject:	Multivariate analysis -- Computer programs. Geotechnical engineering. Big data. Big data. Geotechnical engineering. Multivariate analysis -- Computer programs.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12601418

Hidden Bibliographic Details
ISBN:	9789811374227 9811374228 9789811374210 981137421X 9789811374210 9789811374234 9811374236 9789811374241 9811374244
Digital file characteristics:	text file PDF
Notes:	Includes bibliographical references. Online resource; title from PDF title page (EBSCO, viewed May 7, 2019).
Summary:	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.
Other form:	Printed edition: 9789811374210 Printed edition: 9789811374234 Printed edition: 9789811374241
Standard no.:	10.1007/978-981-13-7422-7

Description
Summary:	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.
Physical Description:	1 online resource.
Bibliography:	Includes bibliographical references.
ISBN:	9789811374227 9811374228 9789811374210 981137421X 9789811374234 9811374236 9789811374241 9811374244