Katalog der Deutschen Nationalbibliothek

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

16291 - 16300 von 16538

	16291	Use of structure-activity landscape index curves and curve integrals to evaluate the performance of multiple machine learning prediction models Enthalten in Journal of cheminformatics Bd. 3, 7.2.2011, Nr. 1, date:12.2011: 1-12
	16292	Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa Enthalten in BMC infectious diseases Bd. 23, 19.7.2023, Nr. 1, date:12.2023: 1-13
	16293	Usefulness of machine learning softwares to screen titles of systematic reviews: a methodological study Enthalten in Systematic Reviews Bd. 12, 15.4.2023, Nr. 1, date:12.2023: 1-14
	16294	User training for machine learning controlled upper limb prostheses: a serious game approach Enthalten in Journal of neuroEngineering and rehabilitation Bd. 18, 12.2.2021, Nr. 1, date:12.2021: 1-15
	16295	Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling Enthalten in BMC medical research methodology Bd. 22, 23.11.2022, Nr. 1, date:12.2022: 1-10
	16296	Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma Enthalten in BMC cancer Bd. 21, 9.8.2021, Nr. 1, date:12.2021: 1-11
	16297	Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case–control cohort analysis Enthalten in BMC nephrology Bd. 14, 23.7.2013, Nr. 1, date:12.2013: 1-9
	16298	Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches Enthalten in BMC medical informatics and decision making Bd. 25, 31.1.2025, Nr. 1, date:12.2025: 1-10
	16299	Using a Sociotechnical Model to Understand Challenges with Sepsis Recognition among Critically Ill Infants Enthalten in ACI Open Bd. 06, 2022, Nr. 02: e57-e65
	16300	Using alcohol consumption diary data from an internet intervention for outcome and predictive modeling: a validation and machine learning study Enthalten in BMC medical research methodology Bd. 20, 11.5.2020, Nr. 1, date:12.2020: 1-9