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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Dettagli Bibliografici
Pubblicato in:Proc Natl Acad Sci U S A
Autori principali: 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
Natura: Artigo
Lingua:Inglês
Pubblicazione: National Academy of Sciences 2020
Soggetti:
Physical Sciences
Accesso online: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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