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Expert-augmented machine learning
Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide wheth...
Autores principales: | Gennatas, Efstathios D., Friedman, Jerome H., Ungar, Lyle H., Pirracchio, Romain, Eaton, Eric, Reichmann, Lara G., Interian, Yannet, Luna, José Marcio, Simone, Charles B., Auerbach, Andrew, Delgado, Elier, van der Laan, Mark J., Solberg, Timothy D., Valdes, Gilmer |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060733/ https://www.ncbi.nlm.nih.gov/pubmed/32071251 http://dx.doi.org/10.1073/pnas.1906831117 |
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