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Machine learning for spatial environ...
~
Kanevski, Mikhail.
Machine learning for spatial environmental data :theory, applications and software /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Machine learning for spatial environmental data :Mikhail Kanevski, Alexei Pozdnoukhov and Vadim Timonin.
其他題名:
theory, applications and software /
作者:
Kanevski, Mikhail.
其他作者:
Timonin, Vadim.
出版者:
Lausanne, Switzerland :EPFL Press ;c2009.
面頁冊數:
xii, 377 p. :ill. (some col.), maps ;25 cm. +1 CD-ROM (4 3/4 in.).
附註:
Title on accompanying CD-ROM: Machine learning office : software for environmental spatial data analysis, full research version.
附註:
At head of title: Environmental engineering.
標題:
Environmental mappingSoftware.
ISBN:
9780849382376 (hbk. : CRC Press) :
Machine learning for spatial environmental data :theory, applications and software /
Kanevski, Mikhail.
Machine learning for spatial environmental data :
theory, applications and software /Mikhail Kanevski, Alexei Pozdnoukhov and Vadim Timonin. - 1st ed. - Lausanne, Switzerland :EPFL Press ;c2009. - xii, 377 p. :ill. (some col.), maps ;25 cm. +1 CD-ROM (4 3/4 in.). - Environmental sciences. - Environmental sciences (Lausanne, Switzerland).
Title on accompanying CD-ROM: Machine learning office : software for environmental spatial data analysis, full research version.
Includes bibliographical references (p. [347]-371) and index.
Learning from geospatial data -- Exploratory spatial data analysis : presentation of data and case studies -- Geostatistics -- Artificial neural networks -- Support vector machines and kernel methods.
Accompanying CD-RM contains Machine learning office software, MLO guide (pdf) and examples of data.
ISBN: 9780849382376 (hbk. : CRC Press) :NT$2993Subjects--Topical Terms:
490499
Environmental mapping
--Software.
LC Class. No.: QE33.2.M3 / K16 2009
Dewey Class. No.: 550
Machine learning for spatial environmental data :theory, applications and software /
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Learning from geospatial data -- Exploratory spatial data analysis : presentation of data and case studies -- Geostatistics -- Artificial neural networks -- Support vector machines and kernel methods.
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