has been cited by the following article(s):
[1]
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The simplicity of XGBoost algorithm versus the complexity of Random Forest, Support Vector Machine, and Neural Networks algorithms in urban forest classification
F1000Research,
2022
DOI:10.12688/f1000research.124604.1
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[2]
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The simplicity of XGBoost algorithm versus the complexity of Random Forest, Support Vector Machine, and Neural Networks algorithms in urban forest classification
F1000Research,
2022
DOI:10.12688/f1000research.124604.1
|
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[3]
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The simplicity of XGBoost algorithm versus the complexity of Random Forest, Support Vector Machine, and Neural Networks algorithms in urban forest classification
F1000Research,
2022
DOI:10.12688/f1000research.124604.1
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[4]
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Analysis of the application of an advanced classifier algorithm to ultra-high resolution unmanned aerial aircraft imagery – a neural network approach
International Journal of Remote Sensing,
2020
DOI:10.1080/01431161.2019.1688413
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[5]
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Analysis of the application of an advanced classifier algorithm to ultra-high resolution unmanned aerial aircraft imagery – a neural network approach
International Journal of Remote Sensing,
2019
DOI:10.1080/01431161.2019.1688413
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[6]
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Urban Vegetation Mapping from Fused Hyperspectral Image and LiDAR Data with Application to Monitor Urban Tree Heights
Journal of Geographic Information System,
2013
DOI:10.4236/jgis.2013.54038
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