Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

M Chica-Olmo - Ore Geology Reviews, 2015 - Elsevier
Abstract Machine learning algorithms (MLAs) such us artificial neural networks (ANNs),
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …
被引用次数：1350 相关文章

Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

V Rodriguez-Galiano, M Sanchez-Castillo… - Ore Geology …, 2015 - infona.pl
Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), regression
trees (RTs), random forest (RF) and support vector machines (SVMs) are powerful data …

Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines

VF Rodriguez-Galiano, M Sanchez-Castillo… - Ore Geology …, 2015 - eprints.soton.ac.uk
Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), regression
trees (RTs), random forest (RF) and support vector machines (SVMs) are powerful data …

Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

V Rodriguez-Galiano… - Ore Geology …, 2015 - ui.adsabs.harvard.edu
Abstract Machine learning algorithms (MLAs) such us artificial neural networks (ANNs),
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …

Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

V Rodriguez-Galiano, M Sanchez-Castillo… - Ore Geology …, 2015 - oceanrep.geomar.de
Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), regression
trees (RTs), random forest (RF) and support vector machines (SVMs) are powerful data …

[引用][C] Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

V Rodriguez-Galiano, M Sanchez-Castillo… - Ore Geology …, 2015 - cir.nii.ac.jp
Machine learning predictive models for mineral prospectivity: An evaluation of neural networks,
random forest, regression trees and support vector machines | CiNii Research CiNii 国立情報学 …

Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

V Rodriguez-Galiano, M Sanchez-Castillo… - Ore Geology …, 2015 - infona.pl
Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), regression
trees (RTs), random forest (RF) and support vector machines (SVMs) are powerful data …