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.
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, pp. 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.
How slow is slow? SFA detects signals that are slower than the driving force Proceedings Article
In: Filipic, Bogdan; Silc, Juri (Ed.): 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).
Search field
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.
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, pp. 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.
How slow is slow? SFA detects signals that are slower than the driving force Proceedings Article
In: Filipic, Bogdan; Silc, Juri (Ed.): Proc. 4th Int. Conf. on Bioinspired Optimization Methods and their Applications, BIOMA, Ljubljana, Slovenia, 2010.
Gestenerkennung mit Slow Feature Analysis (SFA) - Klassifizierung von beschleunigungsbasierten 3D-Gesten des Wii-Controllers Masters Thesis
TH Köln -- University of Applied Sciences, 2010, (Master thesis, 3rd prize in Opitz award 2011).
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).
