Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
im Bestand: Gesamter Bestand

16601 - 16610 von 16798

	16601	Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients Enthalten in Scientific reports Bd. 13, 8.2.2023, Nr. 1, date:12.2023: 1-11
	16602	Using machine learning prediction models for quality control: a case study from the automotive industry Enthalten in Computational management science Bd. 20, 16.3.2023, Nr. 1, date:12.2023: 1-28
	16603	Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review Enthalten in Diagnostic and prognostic research Bd. 4, 2.10.2020, Nr. 1, date:12.2020: 1-12
	16604	Using machine-learning strategies to solve psychometric problems Enthalten in Scientific reports Bd. 12, 7.11.2022, Nr. 1, date:12.2022: 1-9
	16605	Using machine learning techniques for rationalising phenotypic readouts from a rat sleeping model Enthalten in Journal of cheminformatics Bd. 5, 22.3.2013, Nr. 1, date:3.2013: 1
	16606	Using Machine Learning techniques in phenomenological studies on flavour physics Enthalten in Journal of high energy physics Bd. 2022, 19.7.2022, Nr. 7, date:7.2022: 1-43
	16607	USING MACHINE LEARNING TECHNIQUES TO FILTER VEGETATION IN COLORIZED SFM POINT CLOUDS OF SOIL SURFACES Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-1/W2-2023, 2023: 163-170. 8 S.
	16608	Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks Enthalten in Journal of Big Data Bd. 7, 24.8.2020, Nr. 1, date:12.2020: 1-16
	16609	Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data Enthalten in Scientific reports Bd. 14, 4.3.2024, Nr. 1, date:12.2024: 1-11
	16610	Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials Enthalten in Theoretical and applied genetics Bd. 137, 23.7.2024, Nr. 8, date:8.2024: 1-13