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The Open International Soccer Database for machine learning.

, , , и . Mach. Learn., 108 (1): 9-28 (2019)

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Carsten Berrar

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Daniel Dirkmann

In-vitro-Untersuchungen zum Effekt von Hypothermie und Azidose auf Variablen thrombelastometrischer Messungen der Blutgerinnung. Uni Duisburg-Essen, (2009)

Daniel Stanze

Systematische Analyse von Rezeptoren und Effektormechanismen der pulmonalen angeborenen Immunität. Uni Marburg, (2009)
 

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Confidence curves: an alternative to null hypothesis significance testing for the comparison of classifiers.. Mach. Learn., 106 (6): 911-949 (2017)On the Noise Resilience of Ranking Measures.. ICONIP (2), том 9948 из Lecture Notes in Computer Science, стр. 47-55. (2016)Guest editorial: special issue on machine learning for soccer., , , и . Mach. Learn., 108 (1): 1-7 (2019)Self-Organizing Incremental Neural Networks for Continual Learning., и . IJCAI, стр. 6476-6477. ijcai.org, (2019)Should significance testing be abandoned in machine learning?, и . Int. J. Data Sci. Anal., 7 (4): 247-257 (2019)Learning from automatically labeled data: case study on click fraud prediction.. Knowl. Inf. Syst., 46 (2): 477-490 (2016)Detecting click fraud in online advertising: a data mining approach., , , , , , , , , и 8 other автор(ы). J. Mach. Learn. Res., 15 (1): 99-140 (2014)SOINN+, a Self-Organizing Incremental Neural Network for Unsupervised Learning from Noisy Data Streams., и . Expert Syst. Appl., (2020)Incorporating domain knowledge in machine learning for soccer outcome prediction., , и . Mach. Learn., 108 (1): 97-126 (2019)Performance Measures for Binary Classification.. Encyclopedia of Bioinformatics and Computational Biology (1), Elsevier, (2019)