Semi-supervised Self-training Approaches in Small and Unbalanced Datasets: Application to Xerostomia Radiation Side-Effect
Supervised learning algorithms have been widely used as predictors and applied in a myriad of studies. The accuracy of the classification algorithms is strongly dependent on the existence of large and balanced training sets. The existence of a reduced number of labeled data can deeply affect the use...
| Autor principal: | |
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| Outros Autores: | , , , , |
| Formato: | bookPart |
| Idioma: | eng |
| Publicado em: |
2021
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.22/17493 |
| País: | Portugal |
| Oai: | oai:recipp.ipp.pt:10400.22/17493 |