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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, Nr. 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, Nr. 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, Nr. 04/11, 2011, ISSN: 2191-365X.
Tuned Data Mining: A Benchmark Study on Different Tuners Technical Report
Cologne University of Applied Sciences Nr. 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), Bd. 3, Nr. 1, S. 3–10, 2011.
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.
