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Support vector classification for ge...

University of Alberta (Canada).

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Support vector classification for geostatistical modeling of categorical variables.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Support vector classification for geostatistical modeling of categorical variables./
Author:	Gallardo Vizcaino, Enrique Carlos.
Description:	118 p.
Notes:	Source: Masters Abstracts International, Volume: 48-02, page: .
Contained By:	Masters Abstracts International48-02.
Subject:	Engineering, Geological. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR52394
ISBN:	9780494523940

Support vector classification for geostatistical modeling of categorical variables.
Gallardo Vizcaino, Enrique Carlos.

Support vector classification for geostatistical modeling of categorical variables. - 118 p.

Source: Masters Abstracts International, Volume: 48-02, page: .

Thesis (M.Sc.)--University of Alberta (Canada), 2009.

Subsurface geological characterization often requires solving a classification problem to obtain a model of facies that is later populated with continuous properties like porosity or permeability. The classification problem, which consists of assigning a single category to any unsampled location based on observed data, is analyzed and solved in this thesis using geostatistical and machine learning tools.

ISBN: 9780494523940Subjects--Topical Terms:

1035566
Engineering, Geological.

Support vector classification for geostatistical modeling of categorical variables.
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020 $a 9780494523940
035 $a (UMI)AAIMR52394
035 $a AAIMR52394
040 $a UMI $c UMI
100 1 $a Gallardo Vizcaino, Enrique Carlos. $3 1035580
245 1 0 $a Support vector classification for geostatistical modeling of categorical variables.
300 $a 118 p.
500 $a Source: Masters Abstracts International, Volume: 48-02, page: .
502 $a Thesis (M.Sc.)--University of Alberta (Canada), 2009.
520 $a Subsurface geological characterization often requires solving a classification problem to obtain a model of facies that is later populated with continuous properties like porosity or permeability. The classification problem, which consists of assigning a single category to any unsampled location based on observed data, is analyzed and solved in this thesis using geostatistical and machine learning tools.
520 $a This research proposes an easy-to-implement heuristic technique that uses geostatistical criteria, such as correct classification of the observed data and good reproduction of the global proportions of categories, to obtain from the SVC algorithm a boundary classifier. This boundary is used to generate the facies model.
520 $a The case studies show that the implementation of the proposed technique is highly automatic. The responses are comparable in terms of prediction accuracy to those obtained by the conventional geostatistical approach. They also show how simple information from SVC allows for an improvement in the response of conventional geostatistical indicator simulation models.
590 $a School code: 0351.
650 4 $a Engineering, Geological. $3 1035566
690 $a 0466
710 2 $a University of Alberta (Canada). $3 626651
773 0 $t Masters Abstracts International $g 48-02.
790 $a 0351
791 $a M.Sc.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR52394