Gero Szepannek
Gero Szepannek
Professor Statistics, Business Mathematics and Machine Learning, Stralsund University of Applied Sciences
Verified email at - Homepage
Cited by
Cited by
klaR–Classification and Visualization
C Roever, N Raabe, K Luebke, U Ligges, G Szepannek, M Zentgraf
R package, Version 0.3-3. Available from http://cran. R-project. org, 2004
clustMixType: User-Friendly Clustering of Mixed-Type Data in R.
G Szepannek
R J. 10 (2), 200, 2018
Why we should teach causal inference: Examples in linear regression with simulated data
K Lübke, M Gehrke, J Horst, G Szepannek
Journal of Statistics Education 28 (2), 133-139, 2020
On the combination of locally optimal pairwise classifiers
G Szepannek, B Bischl, C Weihs
Engineering Applications of Artificial Intelligence 22 (1), 79-85, 2009
Distances in classification
C Weihs, G Szepannek
Industrial Conference on Data Mining, 1-12, 2009
Local models in register classification by timbre
C Weihs, G Szepannek, U Ligges, K Luebke, N Raabe
Data science and classification, 315-322, 2006
On class imbalance correction for classification algorithms in credit scoring
B Bischl, T Kühn, G Szepannek
Operations Research Proceedings 2014, 37-43, 2016
clustmixtype: K-prototypes clustering for mixed variable-type data
G Szepannek
R package version 0.1-16. URL https://cran. r-project. org/package= clustMixType, 2016
Transparency, auditability, and explainability of machine learning models in credit scoring
M Bücker, G Szepannek, A Gosiewska, P Biecek
Journal of the Operational Research Society 73 (1), 70-90, 2022
Towards automatic speech recognition based on cochlear traveling wave delay trajectories
T Harczos, G Szepannek, F Klefenz
Proceedings of the International Symposium on Auditory and Audiological …, 2007
Local modelling in classification on different feature subspaces
G Szepannek, C Weihs
Industrial conference on data mining, 226-238, 2006
On the practical relevance of modern machine learning algorithms for credit scoring applications
G Szepannek
WIAS Report Series 29, 88-96, 2017
Perceptually based phoneme recognition in popular music
G Szepannek, M Gruhne, B Bischl, S Krey, T Harczos, F Klefenz, ...
Classification as a Tool for Research, 751-758, 2010
Local modelling in classification
G Szepannek, J Schiffner, J Wilson, C Weihs
Industrial Conference on Data Mining, 153-164, 2008
An auditory model based vowel classification
T Harczos, G Szepannek, A Katai, F Klefenz
2006 IEEE Biomedical Circuits and Systems Conference, 69-72, 2006
How much can we see? A note on quantifying explainability of machine learning models
G Szepannek
arXiv preprint arXiv:1910.13376, 2019
Extending features for automatic speech recognition by means of auditory modelling
G Szepannek, T Harczos, F Klefenz, C Weihs
2009 17th European Signal Processing Conference, 1235-1239, 2009
Schallanalyse Neuronale Repräsentation des Hörvorgangs als Basis
G Szepannek, F Klefenz, C Weihs
Informatik-Spektrum 28 (5), 389-395, 2005
Cluster validation for mixed-type data
R Aschenbruck, G Szepannek
Archives of Data Science, Series A 6 (1), 02, 2020
Prediction of spiralling in BTA deep-hole drilling: estimating the system's eigenfrequencies
G Szepannek, N Raabe, O Webber, C Weihs
Technical Report, 2006
The system can't perform the operation now. Try again later.
Articles 1–20