Systematische Optimierung von Modellen für Automatisierungstechnik und IT
Das BMBF-geförderte Projekt SOMA wurde im Zeitraum 2009-2013 durchgeführt. Projektpartner waren die Universität Leiden, die Ruhr-Universität Bochum, die Nurogames GmbH, Köln und die divis GmbH, Dortmund.
Die systematische Optimierung von Modellen für komplexe Anwendungen in Informations- und Automatisierungstechnik, hier mit dem Ziel der Prognose von Zielgrößen oder der optimalen Steuerung von Anlagen oder Prozessen, ist Gegenstand dieses Projektes. Sie stellt nach wie vor eine große Herausforderung für den in der Praxis tätigen Informatiker oder Ingenieur dar. In vielen Fällen handelt es sich nicht allein um ein Problem der optimalen Modellparametrierung, sondern auch um Fragen der intelligenten Datenvorverarbeitung und Datenselektion.
Unter dem Dach von SOMA wurden verschiedene Unterprojekte bearbeitet: Tuned Data Mining (TDMR), Gestenerkennung, Reinforcement Learning für strategische Spiele, Intelligente Methoden der Merkmalsgewinnung, wie z.B. Slow Feature Analysis (SFA).
Themengebiete: Angewandte Informatik, Modellierung, Simulation, Lernende Systeme, Computational Intelligence (evolutionäre Algorithmen, neuronale Netze), Data Mining.
2018
Konen, Wolfgang; Koch, Patrick
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)).
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title = {The TDMR 2.0 Package: Tuned Data Mining in R},
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year = {2018},
date = {2018-04-01},
number = {02/2018},
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Konen, Wolfgang; Koch, Patrick
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)).
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title = {The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R},
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2015
Stork, Jörg; Ramos, Ricardo; Koch, Patrick; Konen, Wolfgang
SVM ensembles are better when different kernel types are combined Incollection
In: Lausen, Berthold; Krolak-Schwerdt, Sabine (Hrsg.): Data Science, Learning by Latent Structures, and Knowledge Discovery, S. 191–201, Springer, 2015.
@incollection{stork15,
title = {SVM ensembles are better when different kernel types are combined},
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url = {http://www.gm.fh-koeln.de/~konen/Publikationen/storkECDA-2013.pdf},
year = {2015},
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2013
Koch, Patrick; Konen, Wolfgang
Subsampling strategies in SVM ensembles Inproceedings
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 23. Workshop Computational Intelligence, S. 119–134, Universitätsverlag Karlsruhe, 2013.
@inproceedings{Koch13a,
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year = {2013},
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Stork, Jörg; Ramos, Ricardo; Koch, Patrick; Konen, Wolfgang
SVM ensembles are better when different kernel types are combined Inproceedings
In: Lausen, Berthold (Hrsg.): European Conference on Data Analysis (ECDA13), GfKl, 2013.
@inproceedings{stork13a,
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2012
Konen, Wolfgang; Koch, Patrick
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).
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Konen, Wolfgang; Koch, Patrick
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).
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Koch, Patrick; Konen, Wolfgang
Efficient sampling and handling of variance in tuning data mining models Inproceedings
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.
@inproceedings{Koch12a,
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Thill, Markus; Koch, Patrick; Konen, Wolfgang
Reinforcement learning with n-tuples on the game Connect-4 Inproceedings
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.
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Koch, Patrick; Bischl, Bernd; Flasch, Oliver; Bartz-Beielstein, Thomas; Weihs, Claus; Konen, Wolfgang
Tuning and Evolution of Support Vector Kernels Journal Article
In: Evolutionary Intelligence, vol. 5, S. 153–170, 2012.
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2011
Konen, Wolfgang
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.
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Konen, Wolfgang
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.
@techreport{Kone11f,
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Koch, Patrick; Bischl, Bernd; Flasch, Oliver; Bartz-Beielstein, Thomas; Konen, Wolfgang
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.
@techreport{Koch11a,
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Konen, Wolfgang; Koch, Patrick; Flasch, Oliver; Bartz-Beielstein, Thomas; Friese, Martina; Naujoks, Boris
Tuned Data Mining: A Benchmark Study on Different Tuners Technical Report
Cologne University of Applied Sciences no. 03/11, 2011.
@techreport{Kone11b,
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Koch, Patrick; Konen, Wolfgang; Naujoks, Boris; Flasch, Oliver; Friese, Martina; Zaefferer, Martin; Bartz-Beielstein, Thomas
Tuned Data Mining in R Inproceedings
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 21. Workshop Computational Intelligence, S. 147–160, Universitätsverlag Karlsruhe, 2011.
@inproceedings{Koch11b,
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Konen, Wolfgang; Koch, Patrick; Flasch, Oliver; Bartz-Beielstein, Thomas; Friese, Martina; Naujoks, Boris
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.
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Konen, Wolfgang; Koch, Patrick
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.
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2010
Koch, Patrick; Konen, Wolfgang; Hein, Kristine
Gesture Recognition on Few Training Data using Slow Feature Analysis and Parametric Bootstrap Inproceedings
In: 2010 International Joint Conference on Neural Networks, 2010.
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Koch, Patrick; Konen, Wolfgang; Flasch, Oliver; Bartz-Beielstein, Thomas
Optimizing Support Vector Machines for Stormwater Prediction Inproceedings
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.
@inproceedings{Koch10b,
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Koch, Patrick; Konen, Wolfgang; Flasch, Oliver; Bartz-Beielstein, Thomas
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.
@inproceedings{Koch10c,
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Konen, Wolfgang; Koch, Patrick; Flasch, Oliver; Bartz-Beielstein, Thomas
Parameter-Tuned Data Mining: A General Framework Inproceedings
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 20. Workshop Computational Intelligence, Universitätsverlag Karlsruhe, 2010.
@inproceedings{Kone10a,
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Konen, Wolfgang; Koch, Patrick
How slow is slow? SFA detects signals that are slower than the driving force Inproceedings
In: Filipic, Bogdan; Silc, Juri (Hrsg.): Proc. 4th Int. Conf. on Bioinspired Optimization Methods and their Applications, BIOMA, Ljubljana, Slovenia, 2010.
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Hein, Kristine
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).
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2009
Konen, Wolfgang
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).
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Konen, Wolfgang
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).
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