Pages that link to "Item:Q702522"
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The following pages link to Comparison of kriging and neural networks with application to the exploitation of a slate mine (Q702522):
Displaying 11 items.
- A hybrid method for grade estimation using genetic algorithm and neural networks (Q732188) (← links)
- Variography for model selection in local polynomial regression with spatial data (Q812061) (← links)
- Creating a quality map of a slate deposit using support vector machines (Q879412) (← links)
- Typing mineral deposits using their associated rocks, grades and tonnages using a probabilistic neural network (Q883155) (← links)
- Ore grade prediction using a genetic algorithm and clustering based ensemble neural network model (Q964855) (← links)
- Shape functional optimization with restrictions boosted with machine learning techniques (Q977443) (← links)
- Partially linear support vector machines applied to the prediction of mine slope movements (Q984150) (← links)
- Comparison of machine learning methods for copper ore grade estimation (Q1715372) (← links)
- Evaluation of interpolation accuracy of neural kriging with application to temperature-distribution analysis (Q1863191) (← links)
- Support vector machines and gradient boosting for graphical estimation of a slate deposit (Q2505847) (← links)
- Comparison of Kriging and artificial neural network models for the prediction of spatial data (Q3390484) (← links)
