2018
The TDMR 2.0 Package: Tuned Data Mining in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 02/2018, 2018, (Last update: April 2018 (original version: 2012)).
The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 03/2018, 2018, (Last update: April 2018 (original version: 2012)).
2015
SVM ensembles are better when different kernel types are combined Book Section
In: Lausen, Berthold; Krolak-Schwerdt, Sabine (Hrsg.): Data Science, Learning by Latent Structures, and Knowledge Discovery, S. 191–201, Springer, 2015.
2013
Subsampling strategies in SVM ensembles Proceedings Article
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 Proceedings Article
In: Lausen, Berthold (Hrsg.): European Conference on Data Analysis (ECDA13), GfKl, 2013.
2012
The TDMR Package: Tuned Data Mining in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 02/2012, 2012, (Last update: June 2017).
The TDMR Tutorial: Examples for Tuned Data Mining in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 03/2012, 2012, (Last update: May, 2016).
Efficient sampling and handling of variance in tuning data mining models Proceedings Article
In: Coello Coello, Carlos; 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 Proceedings Article
In: Coello Coello, Carlos; Cutello, Vincenzo; others, (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 Journal Article
In: Evolutionary Intelligence, vol. 5, S. 153–170, 2012.
2011
Der SFA-Algorithmus für Klassifikation Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 08/11, 2011, ISSN: 2191-365X.
SFA classification with few training data: Improvements with parametric bootstrap Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 09/11, 2011, ISSN: 2191-365X.
On the Tuning and Evolution of Support Vector Kernels Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Scienceand Engineering Science, no. 04/11, 2011, ISSN: 2191-365X.
Tuned Data Mining: A Benchmark Study on Different Tuners Technical Report
Cologne University of Applied Sciences no. 03/11, 2011.
Tuned Data Mining in R Proceedings Article
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 Proceedings Article
In: Krasnogor, Natalio (Hrsg.): GECCO '11: Proceedings of the 13th Annual Conference on Genetic andEvolutionary Computation, S. 1995–2002, 2011.
The slowness principle: SFA can detect different slow components in nonstationary time series Journal Article
In: International Journal of Innovative Computing and Applications (IJICA), vol. 3, no. 1, S. 3–10, 2011.
2010
Gesture Recognition on Few Training Data using Slow Feature Analysis and Parametric Bootstrap Proceedings Article
In: 2010 International Joint Conference on Neural Networks, 2010.
Optimizing Support Vector Machines for Stormwater Prediction Proceedings Article
In: Bartz-Beielstein, Thomas; Chiarandini, M.; Paquete, L.; 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 Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 20. Workshop Computational Intelligence, S. 146–160, Universitätsverlag Karlsruhe, 2010.
Parameter-Tuned Data Mining: A General Framework Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 20. Workshop Computational Intelligence, Universitätsverlag Karlsruhe, 2010.
How slow is slow? SFA detects signals that are slower than the driving force Proceedings Article
In: Filipic, Bogdan; Silc, Juri (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 Technical Report
Cologne University of Applied Sciences no. 05/09, 2009, (e-print published at http://arxiv.org/abs/0911.4397).
On the numeric stability of the SFA implementation sfa-tk Technical Report
Cologne University of Applied Sciences no. 05/10, 2009, (e-print published at http://arxiv.org/abs/0912.1064).
Suchfeld
The TDMR 2.0 Package: Tuned Data Mining in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 02/2018, 2018, (Last update: April 2018 (original version: 2012)).
The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 03/2018, 2018, (Last update: April 2018 (original version: 2012)).
SVM ensembles are better when different kernel types are combined Book Section
In: Lausen, Berthold; Krolak-Schwerdt, Sabine (Hrsg.): Data Science, Learning by Latent Structures, and Knowledge Discovery, S. 191–201, Springer, 2015.
Subsampling strategies in SVM ensembles Proceedings Article
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 Proceedings Article
In: Lausen, Berthold (Hrsg.): European Conference on Data Analysis (ECDA13), GfKl, 2013.
The TDMR Package: Tuned Data Mining in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 02/2012, 2012, (Last update: June 2017).
The TDMR Tutorial: Examples for Tuned Data Mining in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 03/2012, 2012, (Last update: May, 2016).
Efficient sampling and handling of variance in tuning data mining models Proceedings Article
In: Coello Coello, Carlos; 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 Proceedings Article
In: Coello Coello, Carlos; Cutello, Vincenzo; others, (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 Journal Article
In: Evolutionary Intelligence, vol. 5, S. 153–170, 2012.
