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Active learning for clinical text classification: is it better than random sampling?
OBJECTIVE: This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. DESIGN: Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to c...
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| Main Authors: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BMJ Group
2012
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3422824/ https://ncbi.nlm.nih.gov/pubmed/22707743 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1136/amiajnl-2011-000648 |
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