National Science Library of Georgia

Machine learning methods in the environmental sciences : (Record no. 519634)

MARC details
000 -LEADER
fixed length control field 02300nam a22003378i 4500
001 - CONTROL NUMBER
control field CR9780511627217
003 - CONTROL NUMBER IDENTIFIER
control field UkCbUP
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200124160252.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION
fixed length control field m|||||o||d||||||||
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr||||||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 090916s2009||||enk o ||1 0|eng|d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780511627217 (ebook)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 9780521791922 (hardback)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 9780521796422 (paperback)
040 ## - CATALOGING SOURCE
Original cataloging agency UkCbUP
Language of cataloging eng
Description conventions rda
Transcribing agency UkCbUP
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number GE45.D37
Item number H75 2009
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Edition number 22
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Hsieh, William Wei,
Dates associated with a name 1955-
Relator term author.
245 10 - TITLE STATEMENT
Title Machine learning methods in the environmental sciences :
Remainder of title neural networks and kernels /
Statement of responsibility, etc William W. Hsieh.
264 #1 - Production, Publication, Distribution, Manufacture, and Copyright Notice (R)
Place of production, publication, distribution, manufacture (R) Cambridge :
Name of producer, publisher, distributor, manufacturer (R) Cambridge University Press,
Date of production, publication, distribution, manufacture, or copyright notice 2009.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (xiii, 349 pages) :
Other physical details digital, PDF file(s).
336 ## - Content Type (R)
Content type term (R) text
Content type code (R) txt
Source (NR) rdacontent
337 ## - Media Type (R)
Media type term (R) computer
Media type code (R) c
Source (NR) rdamedia
338 ## - Carrier Type (R)
Carrier type term (R) online resource
Carrier type code (R) cr
Source (NR) rdacarrier
500 ## - GENERAL NOTE
General note Title from publisher's bibliographic system (viewed on 05 Oct 2015).
520 ## - SUMMARY, ETC.
Summary, etc Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the environmental sciences. Due to their powerful nonlinear modelling capability, machine learning methods today are used in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and monitoring of snow, ice and forests. The book includes end-of-chapter review questions and an appendix listing web sites for downloading computer code and data sources. A resources website containing datasets for exercises, and password-protected solutions are available. The book is suitable for first-year graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in environmental sciences interested in applying these new methods to their own work.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Environmental sciences.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Print version:
International Standard Book Number 9780521791922
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1017/CBO9780511627217">https://doi.org/10.1017/CBO9780511627217</a>

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