@article{BunteSchneiderHammeretal., author = {Bunte, Kerstin and Schneider, Petra and Hammer, Barbara and Schleif, Frank-Michael and Villmann, Thomas and Biehl, Michael}, title = {Limited Rank Matrix Learning, discriminative dimension reduction and visualization}, series = {Neural Networks}, volume = {26}, journal = {Neural Networks}, doi = {10.1016/j.neunet.2011.10.001}, pages = {159 -- 173}, 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} } @inproceedings{BunteSchleifBiehl, author = {Bunte, Kerstin and Schleif, Frank-Michael and Biehl, Michael}, title = {Adaptive learning for complex-valued data}, series = {20th European Symposium on Artificial Neural Networks, ESANN 2012, Bruges, Belgium, April 25-27, 2012}, booktitle = {20th European Symposium on Artificial Neural Networks, ESANN 2012, Bruges, Belgium, April 25-27, 2012}, language = {en} } @inproceedings{BiehlHammerSchleifetal., author = {Biehl, Michael and Hammer, Barbara and Schleif, Frank-Michael and Schneider, Petra and Villmann, Thomas}, title = {Stationarity of Matrix Relevance LVQ}, series = {2015 International Joint Conference on Neural Networks, IJCNN 2015, Killarney, Ireland, July 12-17, 2015}, booktitle = {2015 International Joint Conference on Neural Networks, IJCNN 2015, Killarney, Ireland, July 12-17, 2015}, doi = {10.1109/IJCNN.2015.7280441}, pages = {1 -- 8}, 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} } @article{SchleifZhuHammer, author = {Schleif, Frank-Michael and Zhu, Xibin and Hammer, Barbara}, title = {Sparse conformal prediction for dissimilarity data}, series = {Annals of Mathematics and Artificial Intelligence}, volume = {74}, journal = {Annals of Mathematics and Artificial Intelligence}, number = {1-2}, doi = {10.1007/s10472-014-9402-1}, pages = {95 -- 116}, language = {en} } @article{GisbrechtSchleif, author = {Gisbrecht, Andrej and Schleif, Frank-Michael}, title = {Metric and non-metric proximity transformations at linear costs}, series = {Neurocomputing}, volume = {167}, journal = {Neurocomputing}, doi = {10.1016/j.neucom.2015.04.017}, pages = {643 -- 657}, language = {en} } @article{Schleif, author = {Schleif, Frank-Michael}, title = {Generic probabilistic prototype based classification of vectorial and proximity data}, series = {Neurocomputing}, volume = {154}, journal = {Neurocomputing}, doi = {10.1016/j.neucom.2014.12.002}, pages = {208 -- 216}, language = {en} } @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} } @article{MuenchRoederHeiligetal., author = {M{\"u}nch, Maximilian and R{\"o}der, Manuel and Heilig, Simon and Raab, Christoph and Schleif, Frank-Michael}, title = {Static and adaptive subspace information fusion for indefinite heterogeneous proximity data}, series = {Neurocomputing}, volume = {555}, journal = {Neurocomputing}, doi = {10.1016/J.NEUCOM.2023.126635}, pages = {126635 -- 126635}, language = {en} } @article{RaabHeusingerSchleif, author = {Raab, Christoph and Heusinger, Moritz and Schleif, Frank-Michael}, title = {Reactive Soft Prototype Computing for Concept Drift Streams}, series = {CoRR}, volume = {abs/2007.05432}, journal = {CoRR}, language = {en} } @article{RaabSchleif, author = {Raab, Christoph and Schleif, Frank-Michael}, title = {Transfer learning extensions for the probabilistic classification vector machine}, series = {CoRR}, volume = {abs/2007.07090}, journal = {CoRR}, language = {en} } @inproceedings{XuYanLiuetal., author = {Xu, Bangguo and Yan, Simei and Liu, Liang and Schleif, Frank-Michael}, title = {Optimizing YOLOv5 for Green AI: A Study on Model Pruning and Lightweight Networks}, series = {Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond - Proceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024}, volume = {1087}, booktitle = {Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond - Proceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024}, editor = {Villmann, Thomas and Kaden, Marika and Geweniger, Tina and Schleif, Frank-Michael}, doi = {10.1007/978-3-031-67159-3\_22}, pages = {196 -- 205}, language = {en} } @inproceedings{Schleif, author = {Schleif, Frank-Michael}, title = {Practical Approaches to Approximate Dominant Eigenvalues in Large Matrices}, series = {Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond - Proceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024}, volume = {1087}, booktitle = {Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond - Proceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024}, editor = {Villmann, Thomas and Kaden, Marika and Geweniger, Tina and Schleif, Frank-Michael}, doi = {10.1007/978-3-031-67159-3\_14}, pages = {118 -- 128}, language = {en} }