Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation
Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduc...
| Autor principal: | |
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| Outros Autores: | , , , , , |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2018
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/1822/71245 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/71245 |