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...

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Detalles Bibliográficos
Publicado en:Proc Natl Acad Sci U S A
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
Formato: Artigo
Lenguaje:Inglês
Publicado: National Academy of Sciences 2020
Materias:
Physical Sciences
Acceso en línea:https://ncbi.nlm.nih.gov/pmc/articles/PMC7060733/
https://ncbi.nlm.nih.gov/pubmed/32071251
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.1906831117
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