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Evaluating the Performance of Support Vector Machines (Svms) and Random Forest (Rf) in Li-Pegmatite Mapping: Preliminary Results

AuthID
P-00R-CGT

Author(s)

Cardoso Fernandes, J

Teodoro, AC

Lima, A

Roda Robles, E

Editor(s)

Schulz, K; Michel, U; Nikolakopoulos, KG

Document Type

Proceedings Paper

Year published

2019

Published

in EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING/GIS APPLICATIONS X in Proceedings of SPIE, ISSN: 0277-786X

Volume: 11156

Conference

Spie Conference on Earth Resources and Environmental Remote Sensing/Gis Applications X Held at Spie Remote Sensing Conference, Date: SEP 10-12, 2019, Location: Strasbourg, FRANCE, Sponsors: SPIE

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Sources

Publication Identifiers

DOI: 10.1117/12.2532577

Scopus: 2-s2.0-85075828819

Wos: WOS:000511101200018

Source Identifiers

ISSN: 0277-786X

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Evaluating the Performance of Support Vector Machines (Svms) and Random Forest (Rf) in Li-Pegmatite Mapping: Preliminary Results

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Evaluating the Performance of Support Vector Machines (Svms) and Random Forest (Rf) in Li-Pegmatite Mapping: Preliminary Results

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