Evaluating the performance of support vector machines (SVMs) and random forest (RF) in Li-pegmatite mapping: preliminary results
Machine learning algorithms (MLAs) have gained great importance in remote sensing-based applications, and also in mineral prospectivity mapping. Studies show that MLAs can outperform classical classification techniques. So, MLAs can be useful in the exploration of strategical raw materials like lith...
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| Other Authors: | , , |
| Format: | book |
| Language: | eng |
| Published: |
2019
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| Online Access: | https://hdl.handle.net/10216/145680 |
| Country: | Portugal |
| Oai: | oai:repositorio-aberto.up.pt:10216/145680 |