Systematical Optimization of Models in Automation and IT
Publications SOMA
2018
The TDMR 2.0 Package: Tuned Data Mining in R Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (02/2018), 2018, (Last update: April 2018 (original version: 2012)).
The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (03/2018), 2018, (Last update: April 2018 (original version: 2012)).
2013
Subsampling strategies in SVM ensembles Inproceedings
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 23. Workshop Computational Intelligence, S. 119–134, Universitätsverlag Karlsruhe, 2013.
SVM ensembles are better when different kernel types are combined Inproceedings
In: Lausen, Berthold (Hrsg.): European Conference on Data Analysis (ECDA13), (under review), 2013.
2012
The TDMR Package: Tuned Data Mining in R Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (02/2012), 2012, (Last update: June 2017).
The TDMR Tutorial: Examples for Tuned Data Mining in R Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (03/2012), 2012, (Last update: May, 2016).
The TDMR Framework: Tuned Data Mining in R Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (02/2012), 2012.
Efficient sampling and handling of variance in tuning data mining models Inproceedings
In: Coello, Carlos A. Coello; Cutello, Vincenzo; others, (Hrsg.): PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina, S. 195–205, Springer, Heidelberg, 2012.
Reinforcement learning with n-tuples on the game Connect-4 Inproceedings
In: Coello, Carlos A. Coello; Cutello, Vincenzo (Hrsg.): PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina, S. 184–194, Springer, Heidelberg, 2012.
Tuning and Evolution of Support Vector Kernels Artikel
In: Evolutionary Intelligence, 5 , S. 153–170, 2012.
2011
Tuned Data Mining in R Inproceedings
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 21. Workshop Computational Intelligence, S. 147–160, Universitätsverlag Karlsruhe, 2011.
Tuned Data Mining: A Benchmark Study on Different Tuners Inproceedings
In: Krasnogor, Natalio (Hrsg.): GECCO '11: Proceedings of the 13th Annual Conference on Genetic andEvolutionary Computation, S. 1995–2002, 2011.
Der SFA-Algorithmus für Klassifikation Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (08/11), 2011, ISSN: 2191-365X.
On the Tuning and Evolution of Support Vector Kernels Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Scienceand Engineering Science, (04/11), 2011, ISSN: 2191-365X.
The slowness principle: SFA can detect different slow components in nonstationary time series Artikel
In: International Journal of Innovative Computing and Applications (IJICA), 3 (1), S. 3–10, 2011.
SFA classification with few training data: Improvements with parametric bootstrap Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (09/11), 2011, ISSN: 2191-365X.
2010
Parameter-Tuned Data Mining: A General Framework Inproceedings
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 20. Workshop Computational Intelligence, Universitätsverlag Karlsruhe, 2010.
Gestenerkennung mit Slow Feature Analysis (SFA) - Klassifizierung von beschleunigungsbasierten 3Đ-Gesten des Wii-Controllers Forschungsbericht
FH Köln 2010.
Optimizing Support Vector Machines for Stormwater Prediction Inproceedings
In: Bartz-Beielstein, Thomas; Chiarandini,; Paquete,; Preuss, Mike (Hrsg.): Proceedings of Workshop on Experimental Methods for the Assessment of Computational Systems joint to PPSN2010, S. 47–59, TU Dortmund, 2010.
Optimization of Support Vector Regression Models for Stormwater Prediction Inproceedings
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 20. Workshop Computational Intelligence, S. 146–160, Universitätsverlag Karlsruhe, 2010.
Gesture Recognition on Few Training Data using Slow Feature Analysis and Parametric Bootstrap Inproceedings
In: 2010 International Joint Conference on Neural Networks, 2010.
How slow is slow? SFA detects signals that are slower than the driving force Inproceedings
In: Filipic, B.; Silc, J. (Hrsg.): Proc. 4th Int. Conf. on Bioinspired Optimization Methods and their Applications, BIOMA, Ljubljana, Slovenia, 2010.
2009
How slow is slow? SFA detects signals that are slower than the driving force Forschungsbericht
Cologne University of Applied Sciences (05/09), 2009, (e-print published at http://arxiv.org/abs/0911.4397).
On the numeric stability of the SFA implementation sfa-tk Forschungsbericht
Cologne University of Applied Sciences (05/10), 2009, (e-print published at http://arxiv.org/abs/0912.1064).