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15081 - 15090 von 15117

	15081	When not to use machine learning: A perspective on potential and limitations Enthalten in Materials Research Society: MRS bulletin Bd. 47, 21.10.2022, Nr. 9, date:9.2022: 968-974
	15082	When physics meets machine learning: a survey of physics-informed machine learning Enthalten in Machine learning for computational science and engineering Bd. 1, 7.5.2025, Nr. 1, date:6.2025: 1-23
	15083	When the crowd gets it wrong – the limits of collective wisdom in machine learning Enthalten in Scientific reports Bd. 15, 1.7.2025, Nr. 1, date:12.2025: 1-11
	15084	Where Is the Geography? A Study of the Predictors of Obesity Using UK Biobank Data and Machine Learning Enthalten in Journal of geovisualization and spatial analysis Bd. 7, 12.6.2023, Nr. 2, date:12.2023: 1-21
	15085	Where you go is who you are: a study on machine learning based semantic privacy attacks Enthalten in Journal of Big Data Bd. 11, 12.3.2024, Nr. 1, date:12.2024: 1-31
	15086	Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms? Enthalten in Biomedical engineering online Bd. 22, 29.8.2023, Nr. 1, date:12.2023: 1-25
	15087	Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach Enthalten in Cardiovascular diabetology Bd. 23, 21.6.2024, Nr. 1, date:12.2024: 1-13
	15088	Whole-orbit radiomics: machine learning-based multi- and fused- region radiomics signatures for intravenous glucocorticoid response prediction in thyroid eye disease Enthalten in Journal of translational medicine Bd. 22, 13.1.2024, Nr. 1, date:12.2024: 1-14
	15089	Why and how to construct an epistemic justification of machine learning? Enthalten in Synthese Bd. 204, 10.8.2024, Nr. 2, date:8.2024: 1-24
	15090	Why does machine learning work really well in many engineering problems? Enthalten in Machine learning for computational science and engineering Bd. 1, 20.8.2025, Nr. 2, date:12.2025: 1-13

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