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ORCIDGero Szepannek
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2020 – today
- 2023
[j10]Rabea Aschenbruck
, Gero Szepannek, Adalbert F. X. Wilhelm:
Imputation Strategies for Clustering Mixed-Type Data with Missing Values. J. Classif. 40(1): 2-24 (2023)- [c14]Max Schröder, Sascha Genehr, Rüdiger Köhling, Stefan Schmidt, Ralf Schneider, Sascha Spors, Gero Szepannek, Dagmar Waltemath, Frank Krüger:
A Survey on the Current Status of Research Data Management in Mecklenburg-Vorpommern Preliminary Results for a Questionnaire Study Among Researchers. CoRDI 2023 - 2022
- [j9]Michael Bücker, Gero Szepannek, Alicja Gosiewska, Przemyslaw Biecek:
Transparency, auditability, and explainability of machine learning models in credit scoring. J. Oper. Res. Soc. 73(1): 70-90 (2022) - [j8]Gero Szepannek, Karsten Lübke:
Explaining Artificial Intelligence with Care. Künstliche Intell. 36(2): 125-134 (2022) - [j7]Laurens Martin Tetzlaff, Gero Szepannek:
mlr3shiny - State-of-the-art machine learning made easy. SoftwareX 20: 101246 (2022) - [c13]Gero Szepannek, Laurens Martin Tetzlaff, Alexander Frahm, Karsten Lübke:
Teaching Machine Learning with mlr3 using Shiny. Teaching ML 2022: 42-45 - 2021
- [j6]Gero Szepannek, Karsten Luebke:
Facing the Challenges of Developing Fair Risk Scoring Models. Frontiers Artif. Intell. 4: 681915 (2021) - 2020
- [i3]Gero Szepannek:
An Overview on the Landscape of R Packages for Credit Scoring. CoRR abs/2006.11835 (2020) - [i2]Michael Bücker, Gero Szepannek, Alicja Gosiewska, Przemyslaw Biecek:
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring. CoRR abs/2009.13384 (2020)
2010 – 2019
- 2019
- [i1]Gero Szepannek:
How Much Can We See? A Note on Quantifying Explainability of Machine Learning Models. CoRR abs/1910.13376 (2019) - 2018
- [j5]Gero Szepannek:
clustMixType: User-Friendly Clustering of Mixed-Type Data in R. R J. 10(2): 200 (2018) - 2016
- [j4]Gero Szepannek:
Editorial. Trans. Case Based Reason. 5(1): 1-2 (2016) - 2014
- [c12]Bernd Bischl, Tobias Kühn, Gero Szepannek:
On Class Imbalance Correction for Classification Algorithms in Credit Scoring. OR 2014: 37-43
2000 – 2009
- 2009
- [j3]Claus Weihs, Gero Szepannek:
Distances in Classification. Trans. Case Based Reason. 2(1): 3-14 (2009) - [j2]Gero Szepannek, Bernd Bischl, Claus Weihs:
On the combination of locally optimal pairwise classifiers. Eng. Appl. Artif. Intell. 22(1): 79-85 (2009) - [c11]Gero Szepannek, Tamás Harczos, Frank Klefenz, Claus Weihs:
Extending features for automatic speech recognition by means of auditory modelling. EUSIPCO 2009: 1235-1239 - [c10]Claus Weihs, Gero Szepannek:
Distances in Classification. ICDM 2009: 1-12 - 2008
- [c9]Julia Schiffner, Gero Szepannek, Thierry Monthé, Claus Weihs:
Localized Logistic Regression for Categorical Influential Factors. GfKl 2008: 185-195 - [c8]Gero Szepannek, Julia Schiffner, Julie Wilson, Claus Weihs:
Local Modelling in Classification. ICDM 2008: 153-164 - 2007
- [c7]Gero Szepannek, Bernd Bischl, Claus Weihs:
On the Combination of Locally Optimal Pairwise Classifiers. MLDM 2007: 104-116 - 2006
- [c6]Gero Szepannek, Tamás Harczos, Frank Klefenz, András Kátai, Patrick Schikowski, Claus Weihs:
Vowel Classification by a Neurophysiologically Parameterized Auditory Model. GfKl 2006: 653-660 - [c5]Gero Szepannek, Claus Weihs:
Local Modelling in Classification on Different Feature Subspaces. ICDM 2006: 226-238 - [p1]Claus Weihs, Gero Szepannek, Uwe Ligges, Karsten Luebke, Nils Raabe:
Local Models in Register Classification by Timbre. Data Science and Classification 2006: 315-322 - 2005
- [j1]Gero Szepannek, Frank Klefenz, Claus Weihs:
Schallanalyse Neuronale Repräsentation des Hörvorgangs als Basis. Inform. Spektrum 28(5): 389-395 (2005) - [c4]Gero Szepannek, Claus Weihs:
Variable Selection for Discrimination of More Than Two Classes Where Data are Sparse. GfKl 2005: 700-707 - [c3]Gero Szepannek, Karsten Luebke, Claus Weihs:
Understanding Patterns with Different Subspace Classification. MLDM 2005: 110-119 - 2004
- [c2]Gero Szepannek, Karsten Luebke:
Different Subspace Classification. GfKl 2004: 224-231 - [c1]Christian Röver, Gero Szepannek:
Application of a Genetic Algorithm to Variable Selection in Fuzzy Clustering. GfKl 2004: 674-681
Coauthor Index
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