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Artikel 16431 Testing conditional independence in supervised learning algorithms
Enthalten in Machine learning Bd. 110, 2.8.2021, Nr. 8, date:8.2021: 2107-2129
Online Ressource
Artikel 16432 Testing Earthquake Prediction Algorithms
Enthalten in Geological Society of India: Journal of the Geological Society of India Bd. 97, 19.12.2021, Nr. 12, date:12.2021: 1514-1519
Online Ressource
Artikel 16433 Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study
Enthalten in Abdominal radiology Bd. 45, 10.12.2019, Nr. 3, date:3.2020: 789-798
Online Ressource
Artikel 16434 TGV: theory, principles and algorithms
Enthalten in International journal on software tools for technology transfer Bd. 7, 20.10.2004, Nr. 4, date:8.2005: 297-315
Online Ressource
Artikel 16435 The accuracy of fully-automated algorithms for the surveillance of central venous catheter-related bloodstream infection in hospitalised patients
Enthalten in Antimicrobial resistance and infection control Bd. 13, 5.2.2024, Nr. 1, date:12.2024: 1-12
Online Ressource
Artikel 16436 The algorithms for calculating synthetic seismograms from a dipole source using the derivatives of Green’s function
Enthalten in Izvestiya / Physics of the solid earth Bd. 53, 13.7.2017, Nr. 4, date:7.2017: 556-564
Online Ressource
Artikel 16437 The Algorithms for the Eulerian Cycle and Eulerian Trail Problems for a Multiple Graph
Enthalten in Automatic control and computer sciences Bd. 58, 12.2.2025, Nr. 7, date:12.2024: 889-903
Online Ressource
Artikel 16438 The ambiguity of intelligent algorithms: job killer or supporting assistant
Enthalten in European Journal of Futures Research Bd. 4, 30.10.2016, Nr. 1, date:12.2016: 1-8
Online Ressource
Artikel 16439 The analysis of the stability of some integer programming algorithms with respect to the objective function
Enthalten in Russian mathematics Bd. 55, 29.3.2011, Nr. 4, date:4.2011: 18-25
Online Ressource
Artikel 16440 The application of deep learning algorithms to classify subsurface drilling lost circulation severity in large oil field datasets
Enthalten in SN applied sciences Bd. 3, 26.8.2021, Nr. 9, date:9.2021: 1-22
Online Ressource


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