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Disambiguation of "Schollmeyer, Georg"

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Testing of Coarsening Mechanisms: Coarsening at Random Versus Subgroup Independence.

, , , and . SMPS, volume 456 of Advances in Intelligent Systems and Computing, page 415-422. Springer, (2016)

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Georg Schollmeyer

Georg Gustav Schollmeyer

Wolfgang Schollmeyer

Eberhard Schollmeyer

Eckhard Schollmeyer

 

Other publications of authors with the same name

A Short Note on the Equivalence of the Ontic and the Epistemic View on Data Imprecision for the Case of Stochastic Dominance for Interval-Valued Data.. ISIPTA, volume 103 of Proceedings of Machine Learning Research, page 330-337. PMLR, (2019)Comparing machine learning algorithms by union-free generic depth., , , and . Int. J. Approx. Reason., (2024)Towards Improving Electoral Forecasting by Including Undecided Voters and Interval-valued Prior Knowledge., , and . ISIPTA, volume 147 of Proceedings of Machine Learning Research, page 201-209. PMLR, (2021)Robust statistical comparison of random variables with locally varying scale of measurement., , , , and . UAI, volume 216 of Proceedings of Machine Learning Research, page 941-952. PMLR, (2023)Multi-target Decision Making Under Conditions of Severe Uncertainty., , and . MDAI, volume 13890 of Lecture Notes in Computer Science, page 45-57. Springer, (2023)Testing of Coarsening Mechanisms: Coarsening at Random Versus Subgroup Independence., , , and . SMPS, volume 456 of Advances in Intelligent Systems and Computing, page 415-422. Springer, (2016)Constructing Simulation Data with Dependence Structure for Unreliable Single-Cell RNA-Sequencing Data Using Copulas, , and . (2019)Modellierung unsicheren Wissens durch kohärente Prävisionen. (April 2011)Concepts for Decision Making under Severe Uncertainty with Partial Ordinal and Partial Cardinal Preferences., , and . ISIPTA, volume 62 of Proceedings of Machine Learning Research, page 181-192. PMLR, (2017)Depth functions for partial orders with a descriptive analysis of machine learning algorithms., , , and . ISIPTA, volume 215 of Proceedings of Machine Learning Research, page 59-71. PMLR, (2023)