Systematical Optimization of Models in Automation and IT
Publications SOMA
| 1. | : General Board Game Playing for Education and Research in Generic AI Game Learning. In: Perez, Diego; Mostaghim, Sanaz; Lucas, Simon (Hrsg.): IEEE Conference on Games, London, 2019. |
| 2. | : Solving Optimization Problems with High Conditioning by Means of Online Whitening. In: Lopez-Ibanez, Manuel (Hrsg.): Genetic and Evolutionary Computation Conference 2019 (GECCO'19), Prague, S. 243-244, ACM, 2019. |
| 3. | : SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning. arXiv preprint arXiv:1904.08397 2019. |
| 4. | : How to Solve the Dilemma of Margin-Based Equality Constraint Handling Methods. In: at-Automatisierungstechnik, submitted , 2019. |
| 5. | : Deep Learning mit Keras und Tensorflow. Vortrag auf DEBRL2019 (Digital Exchange Bergisches Rheinland 2019), 2019. |
| 6. | : The GBG Class Interface Tutorial V2.0: General Board Game Playing and Learning. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) 2019. |
| 7. | : Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen. TH Köln -- University of Applied Sciences, 2019, (Master thesis). |
| 8. | : How to Solve the Dilemma of Margin-Based Equality Handling Methods. In: Hoffmann, Frank; Hüllermeier, Eyke; Mikut, Ralf (Hrsg.): Proceedings - 28. Workshop Computational Intelligence, Dortmund, 29. - 30. November 2018, S. 257-270, KIT Scientific Publishing, Karlsruhe, 2018, ISBN: 978-3-7315-0845-8, (Young Author Award). |
| 9. | : Online Adaptable Time Series Anomaly Detection with Discrete Wavelet Transforms and Multivariate Gaussian Distributions. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) TH Köln - University of Applied Science, 2018, (submitted to Archives of Data Sciences, Series A (ECDA'2018), preprint available at http://www.gm.fh-koeln.de/ciopwebpub/Thill18a.d/AoDS2018.pdf). |
| 10. | : The TDMR 2.0 Package: Tuned Data Mining in R. 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)). |
| 11. | : The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R. 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)). |
| 12. | : Comparing Kriging and Radial Basis Function Surrogates. In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 243-259, Universitätsverlag Karlsruhe, 2017. |
| 13. | : Anomaly Detection in Time Series with Discrete Wavelet Transforms and Maximum Likelihood Estimation. In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 67-71, Universitätsverlag Karlsruhe, 2017. |
| 14. | : Constraint Handling in Efficient Global Optimization. In: Bosman, Peter A N (Hrsg.): Genetic and Evolutionary Computation Conference 2017 (GECCO'17), Berlin, S. 1, ACM, 2017. |
| 15. | : Time Series Anomaly Detection with Discrete Wavelet Transforms and Maximum Likelihood Estimation. In: Valenzuela, Olga; Rojas, Ignacio; others, (Hrsg.): International Work-Conference on Time Series (ITISE2017), 2017. |
| 16. | : The GBG Class Interface Tutorial: General Board Game Playing and Learning. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) TH Köln - University of Applied Science, 2017, (e-print published at http://www.gm.fh-koeln.de/ciopwebpub/Kone17a.d/TR-GBG.pdf). |
| 17. | : Self-adjusting parameter control for surrogate-assisted constrained optimization under limited budgets. In: Applied Soft Computing, 61 , S. 377–393, 2017, ISSN: 1568-4946. |
| 18. | : Selbstlernende Agenten für das skalierbare Spiel Hex: Untersuchung verschiedener KI-Verfahren im GBG-Framework. TH Köln -- University of Applied Sciences, 2017, (Bachelor thesis). |
| 19. | : KI-Agenten fur das Spiel 2048: Untersuchung von Lernalgorithmen für nichtdeterministische Spiele. TH Köln -- University of Applied Sciences, 2017, (Bachelor thesis). |
| 20. | : Online anomaly detection on the Webscope S5 dataset: A comparative study. In: IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS 2017), S. 1, Springer 2017. |
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 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 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 Coello, Carlos Coello A; 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 Coello, Carlos Coello A; 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 Evolutionary Intelligence, 5 , S. 153–170, 2012. |
2011 |
Tuned Data Mining in R Inproceedings 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 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 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 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 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 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 2010 International Joint Conference on Neural Networks, 2010. |
How slow is slow? SFA detects signals that are slower than the driving force Inproceedings 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). |