Ir directamente a la navegación principal
Ir directamente a la búsqueda
Ir directamente al contenido principal
Clasificar por
INIS
comparative evaluations
100%
copper
100%
machine learning
100%
resources
100%
peru
100%
mineral resources
100%
deposits
100%
datasets
60%
accuracy
60%
kriging
60%
neural networks
40%
forests
40%
randomness
40%
tools
20%
applications
20%
classification
20%
distance
20%
trains
20%
geometry
20%
lagrangian
20%
Keyphrases
Machine Learning Techniques
100%
Peru
100%
Copper Deposit
100%
Deep Neural Network
66%
Mineral Resources
66%
Random Forest Model
33%
Lagrangian
33%
Overall Accuracy
33%
Geostatistics
33%
Classification Results
33%
Average Distance
33%
XGBoost
33%
Kriging
33%
Kriging Variance
33%
Ordinary Kriging
33%
Three-machine
33%
Extreme Gradient Boosting(XGBoost)
33%
Tonnage
33%
Random Forest Neural Networks
33%
Computer Science
Machine Learning Technique
100%
Extreme Gradient Boosting
100%
Random Decision Forest
66%
Deep Neural Network
66%
classification result
33%
Primary Objective
33%
Average Accuracy
33%
Earth and Planetary Sciences
Xgboost
50%
Extreme Gradient Boosting
25%
Economics, Econometrics and Finance
Agricultural and Biological Sciences