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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Veröffentlicht in:Proc Natl Acad Sci U S A
Hauptverfasser: 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
Format: Artigo
Sprache:Inglês
Veröffentlicht: National Academy of Sciences 2020
Schlagworte:
Physical Sciences
Online Zugang: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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