@inproceedings{BifetHammerSchleif, author = {Bifet, Albert and Hammer, Barbara and Schleif, Frank-Michael}, title = {Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets}, series = {27th European Symposium on Artificial Neural Networks, ESANN 2019, Bruges, Belgium, April 24-26, 2019}, booktitle = {27th European Symposium on Artificial Neural Networks, ESANN 2019, Bruges, Belgium, April 24-26, 2019}, language = {en} } @inproceedings{RaabHeusingerSchleif, author = {Raab, Christoph and Heusinger, Moritz and Schleif, Frank-Michael}, title = {Reactive Soft Prototype Computing for frequent reoccurring Concept Drift}, series = {27th European Symposium on Artificial Neural Networks, ESANN 2019, Bruges, Belgium, April 24-26, 2019}, booktitle = {27th European Symposium on Artificial Neural Networks, ESANN 2019, Bruges, Belgium, April 24-26, 2019}, language = {en} } @inproceedings{SchleifRaabTino, author = {Schleif, Frank-Michael and Raab, Christoph and Ti{\~n}o, Peter}, title = {Sparsification of Indefinite Learning Models}, series = {Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, August 17-19, 2018, Proceedings}, booktitle = {Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, August 17-19, 2018, Proceedings}, editor = {Bai, Xiao and Hancock, Edwin R. and Kam Ho, Tin and Wilson, Richard C. and Biggio, Battista and Robles-Kelly, Antonio}, doi = {10.1007/978-3-319-97785-0_17}, pages = {173 -- 183}, language = {en} } @inproceedings{RaabSchleif, author = {Raab, Christoph and Schleif, Frank-Michael}, title = {Transfer learning for the probabilistic classification vector machine}, series = {7th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2018, 11-13 June 2018, Maastricht, The Netherlands}, volume = {91}, booktitle = {7th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2018, 11-13 June 2018, Maastricht, The Netherlands}, editor = {Gammerman, Alex J. and Vovk, Vladimir and Luo, Zhiyuan and Smirnov, Evgueni N. and Peeters, Ralf L. M.}, pages = {187 -- 200}, language = {en} } @inproceedings{RaabSchleif, author = {Raab, Christoph and Schleif, Frank-Michael}, title = {Sparse Transfer Classification for Text Documents}, series = {KI 2018: Advances in Artificial Intelligence - 41st German Conference on AI, Berlin, Germany, September 24-28, 2018, Proceedings}, booktitle = {KI 2018: Advances in Artificial Intelligence - 41st German Conference on AI, Berlin, Germany, September 24-28, 2018, Proceedings}, editor = {Trollmann, Frank and Turhan, Anni-Yasmin}, doi = {10.1007/978-3-030-00111-7_15}, pages = {169 -- 181}, language = {en} } @inproceedings{MohammadiPeletierSchleifetal., author = {Mohammadi, Mohammad and Peletier, Reynier and Schleif, Frank-Michael and Petkov, Nicolai and Bunte, Kerstin}, title = {Globular Cluster Detection in the Gaia Survey}, series = {26th European Symposium on Artificial Neural Networks, ESANN 2018, Bruges, Belgium, April 25-27, 2018}, booktitle = {26th European Symposium on Artificial Neural Networks, ESANN 2018, Bruges, Belgium, April 25-27, 2018}, language = {en} } @article{SchleifGisbrechtTino, author = {Schleif, Frank-Michael and Gisbrecht, Andrej and Ti{\~n}o, Peter}, title = {Supervised low rank indefinite kernel approximation using minimum enclosing balls}, series = {Neurocomputing}, volume = {318}, journal = {Neurocomputing}, doi = {10.1016/j.neucom.2018.08.057}, pages = {213 -- 226}, language = {en} } @article{RaabRoederSchleif, author = {Raab, Christoph and R{\"o}der, Manuel and Schleif, Frank-Michael}, title = {Domain adversarial tangent subspace alignment for explainable domain adaptation}, series = {Neurocomputing}, volume = {506}, journal = {Neurocomputing}, doi = {10.1016/j.neucom.2022.07.074}, pages = {418 -- 429}, language = {en} } @article{SchleifTino, author = {Schleif, Frank-Michael and Ti{\~n}o, Peter}, title = {Indefinite Core Vector Machine}, series = {Pattern Recognition}, volume = {71}, journal = {Pattern Recognition}, doi = {10.1016/j.patcog.2017.06.003}, pages = {187 -- 195}, language = {en} } @inproceedings{HartmannDobhanEngelmannetal., author = {Hartmann, J{\"u}rgen and Dobhan, Alexander and Engelmann, Bastian and Eberhardt, Lars and Heusinger, Moritz and Raab, C and Schleif, Frank-Michael and T{\"u}rk, M.}, title = {Optimierung von Prozessen und Werkzeugmaschinen durch Bereitstellung, Analyse und Soll-Ist-Vergleich von Produktionsdaten: Digitalkonferenz}, language = {en} } @article{SchleifHammerGonzalezMonroyetal., author = {Schleif, Frank-Michael and Hammer, Barbara and Gonzalez Monroy, Javier and Gonz{\´a}lez Jim{\´e}nez, Javier and Blanco-Claraco, Jos{\´e}-Luis and Biehl, Michael and Petkov, Nicolai}, title = {Odor recognition in robotics applications by discriminative time-series modeling}, series = {Pattern Analysis and Applications}, volume = {19}, journal = {Pattern Analysis and Applications}, number = {1}, doi = {10.1007/s10044-014-0442-2}, pages = {207 -- 220}, language = {en} } @article{MuenchRaabBiehletal., author = {M{\"u}nch, Maximilian and Raab, Christoph and Biehl, Michael and Schleif, Frank-Michael}, title = {Data-Driven Supervised Learning for Life Science Data}, series = {Frontiers in Applied Mathematics and Statistics}, volume = {6}, journal = {Frontiers in Applied Mathematics and Statistics}, doi = {10.3389/fams.2020.553000}, pages = {553000 -- 553000}, language = {en} } @article{MuenchStraatBiehletal., author = {M{\"u}nch, Maximilian and Straat, Michiel and Biehl, Michael and Schleif, Frank-Michael}, title = {Complex-valued embeddings of generic proximity data}, series = {CoRR}, volume = {abs/2008.13454}, journal = {CoRR}, language = {en} } @article{HofmannEberhardtHeusingeretal., author = {Hofmann, Jan and Eberhardt, Lars and Heusinger, Moritz and Dobhan, Alexander and Engelmann, Bastian and Schleif, Frank-Michael}, title = {Optimierung von Prozessen und Werkzeugmaschinen durch Bereitstellung, Analyse und Soll-Ist-Vergleich von Produktionsdaten}, series = {FHWS Science Journal}, volume = {5}, journal = {FHWS Science Journal}, number = {2}, issn = {2196-6095}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-20012}, pages = {135 -- 142}, abstract = {Mit einem Umsatz von 103 Milliarden Euro ist die Metallindustrie eine der gr{\"o}ßten deutschen Industriebranchen. Diese ist von volatilen Marktbedingungen und hohem Wettbewerb gepr{\"a}gt [1][2]. Kleine und mittlere produzierende Unternehmen (sogenannte KMU) sehen zunehmend gravierende Probleme bei der Einhaltung von Lieferterminen bedingt durch hohe Durchlaufzeiten in der Produktion [3]. Neben kaufm{\"a}nnischen Planungssystemen zur Erstellung von Produktionspl{\"a}nen nutzen Unternehmen als Planungsgrundlage weiterhin Excel mit 31 \% und manuelle Prozesse mit 10 \% [4]. Gleiches gilt f{\"u}r Produktwechselvorg{\"a}nge auf Maschinen (R{\"u}sten). Aufgrund dieser Aspekte ist es notwendig, die Rentabilit{\"a}t der KMU in der Metallindustrie zu steigern. Das wird durch effiziente Produktionsplanung und -steuerung, sowie der daraus resultierenden hohen Reaktionsf{\"a}higkeit und Flexibilit{\"a}t realisiert. Daher ist die Produktionsplanung auf die Markt- und Kundenanforderungen und die Anlageneffektivit{\"a}t auf ein hohes und stabiles Niveau auszurichten [5]. Hier bietet die Erfassung von Echtzeitdaten eine ad{\"a}quate Reaktion auf die genannten Anforderungen. Ebenfalls liefert sie großes Potenzial f{\"u}r die Produktionsplanung und -steuerung, um die Disposition und Koordination von Arbeitsauftr{\"a}gen zu optimieren. Weiterhin werden St{\"o}rgr{\"o}ßen oder unvorhergesehene Planungsabweichungen reduziert [4][6]. Zus{\"a}tzlich ist eine erh{\"o}hte Transparenz und Verbesserung menschlicher Entscheidungsprozesse notwendig. Dies kann durch datengetriebene Methoden unterst{\"u}tzt und sichergestellt werden [7]. Ein Ansatz zur Optimierung des Produktionsergebnisses kann durch die Erh{\"o}hung der Anlagenproduktivit{\"a}t selbst realisiert werden. Dazu muss die Verf{\"u}gbarkeit der Anlagen durch Lokalisierung und Reduzierung von Verlusten erh{\"o}ht werden. Die Umr{\"u}stungsprozesse tragen stark negativ zur Verf{\"u}gbarkeit einer Produktion bei. Eine Steigerung der Gesamtanlageneffektivit{\"a}t (overall equipment effectiveness oder kurz OEE) in einer Fertigungsumgebung ist jedoch m{\"o}glich durch eine intelligente Nutzung von Sensordaten mit Techniken wie z. B. Machine Learning (ML).}, language = {de} } @inproceedings{KlehrEngelmannSchleifetal., author = {Klehr, Lukas and Engelmann, Bastian and Schleif, Frank-Michael and Regulin, Daniel}, title = {Contextualized Segmentation of Milling Processes Using Discrete Rule-Based Pattern Recognition}, series = {Engineering Applications of Neural Networks - 26th International Conference, EANN 2025, Limassol, Cyprus, June 26-29, 2025, Proceedings, Part II}, volume = {2582}, booktitle = {Engineering Applications of Neural Networks - 26th International Conference, EANN 2025, Limassol, Cyprus, June 26-29, 2025, Proceedings, Part II}, editor = {Iliadis, Lazaros S. and Maglogiannis, Ilias and Kyriacou, Efthyvoulos and Jayne, Chrisina}, doi = {10.1007/978-3-031-96199-1\_18}, pages = {238 -- 254}, language = {en} } @inproceedings{MuenchRoederSchleif, author = {M{\"u}nch, Maximilian and R{\"o}der, Manuel and Schleif, Frank-Michael}, title = {Unlocking the Potential of Non-PSD Kernel Matrices: A Polar Decomposition-based Transformation for Improved Prediction Models}, series = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023}, booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023}, editor = {Frommholz, Ingo and Hopfgartner, Frank and Lee, Mark and Oakes, Michael and Lalmas, Mounia and Zhang, Min and Santos, Rodrygo L. T.}, doi = {10.1145/3583780.3615102}, pages = {1867 -- 1876}, language = {en} } @inproceedings{PolatoHammerSchleif, author = {Polato, Mirko and Hammer, Barbara and Schleif, Frank-Michael}, title = {Machine learning in distributed, federated and non-stationary environments - recent trends}, series = {32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2024, Bruges, Belgium, October 9-11, 2024}, booktitle = {32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2024, Bruges, Belgium, October 9-11, 2024}, doi = {10.14428/ESANN/2024.ES2024-3}, language = {en} } @article{OnetoBunteSchleif, author = {Oneto, Luca and Bunte, Kerstin and Schleif, Frank-Michael}, title = {Advances in artificial neural networks, machine learning and computational intelligence: Selected papers from the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)}, series = {Neurocomputing}, volume = {342}, journal = {Neurocomputing}, doi = {10.1016/J.NEUCOM.2019.01.081}, pages = {1 -- 5}, language = {en} } @inproceedings{EweckerWinklerVaethetal., author = {Ewecker, Lukas and Winkler, Timo and V{\"a}th, Philipp and Schwager, Robin and Br{\"u}hl, Tim and Schleif, Frank-Michael}, title = {How Important is the Temporal Context to Anticipate Oncoming Vehicles at Night?}, series = {IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023, Honolulu, Oahu, HI, USA, October 1-4, 2023}, booktitle = {IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023, Honolulu, Oahu, HI, USA, October 1-4, 2023}, doi = {10.1109/SMC53992.2023.10394461}, pages = {1000 -- 1007}, language = {en} } @inproceedings{RoederSchleif, author = {R{\"o}der, Manuel and Schleif, Frank-Michael}, title = {Sparse Uncertainty-Informed Sampling from Federated Streaming Data}, series = {32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2024, Bruges, Belgium, October 9-11, 2024}, booktitle = {32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2024, Bruges, Belgium, October 9-11, 2024}, doi = {10.14428/ESANN/2024.ES2024-9}, language = {en} }