Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines
Víctor Rodríguez‐Galiano, M. Sánchez-Castillo low, Mario Chica‐Olmo, M. Chica-Rivas low
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What this paper cites, inside the corpus
| Paper | Year | Cited |
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| Random Forests | 2001 | 131,109 |
| Support-vector networks | 1995 | 41,028 |
| The Nature of Statistical Learning Theory | 1995 | 39,409 |
| Support-Vector Networks | 1995 | 33,914 |
| Learning representations by back-propagating errors | 1986 | 31,688 |
| C4.5: Programs for Machine Learning | 1992 | 23,704 |
| Classification and Regression Trees. | 1986 | 21,044 |
| Bagging Predictors | 1996 | 17,191 |
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| A training algorithm for optimal margin classifiers | 1992 | 11,664 |
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| An introduction to variable and feature selection | 2003 | 7,858 |
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| Geochemistry and Geologic Mapping | Computer Science |
| Soil Geostatistics and Mapping | Environmental Science |
| Mineral Processing and Grinding | Engineering |
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