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2182 Publications
2019 | Mastersthesis | LibreCat-ID: 15883
Kumar Jeyakumar, S. (2019). Incremental learning with Support Vector Machine on embedded platforms.
LibreCat
2019 | Conference (Editor) | LibreCat-ID: 14829
Scheideler, C., & Berenbrink, P. (Eds.). (2019). The 31st ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2019, Phoenix, AZ, USA, June 22-24, 2019. ACM. https://doi.org/10.1145/3323165
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| DOI
2019 | Working Paper | LibreCat-ID: 14902 | 
Mair, C., Scheffler, W., Senger, I., & Sureth-Sloane, C. (2019). Analyse der Veränderung der zwischenstaatlichen Gewinnaufteilung bei Einführung einer standardisierten Gewinnverteilungsmethode am Beispiel des Einsatzes von 3D-Druckern (Vol. 42).
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2019 | Journal Article | LibreCat-ID: 15001
Couso, I., Borgelt, C., Hüllermeier, E., & Kruse, R. (2019). Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine, 31–44. https://doi.org/10.1109/mci.2018.2881642
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| DOI
2019 | Journal Article | LibreCat-ID: 15002 |
Waegeman, W., Dembczynski, K., & Hüllermeier, E. (2019). Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery, 33(2), 293–324. https://doi.org/10.1007/s10618-018-0595-5
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2019 | Conference Paper | LibreCat-ID: 15003
Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., & Waegeman, W. (2019). Set-Valued Prediction in Multi-Class Classification. In Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019.
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2019 | Book Chapter | LibreCat-ID: 15004
Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Feature Selection for Analogy-Based Learning to Rank. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_22
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| DOI
2019 | Book Chapter | LibreCat-ID: 15005
Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Analogy-Based Preference Learning with Kernels. In KI 2019: Advances in Artificial Intelligence. Cham. https://doi.org/10.1007/978-3-030-30179-8_3
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| DOI
2019 | Conference Paper | LibreCat-ID: 15007 |
Melnikov, V., & Hüllermeier, E. (2019). Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101). https://doi.org/10.1016/j.jmva.2019.02.017
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2019 | Conference Paper | LibreCat-ID: 15013
Brinker, K., & Hüllermeier, E. (2019). A Reduction of Label Ranking to Multiclass Classification. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany.
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2019 | Conference Paper | LibreCat-ID: 15014
Hüllermeier, E., Couso, I., & Diestercke, S. (2019). Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In Proceedings SUM 2019, International Conference on Scalable Uncertainty Management.
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2019 | Book Chapter | LibreCat-ID: 16251
Müller, O. (2019). Structuring Unstructured Data—Or: How Machine Learning Can Make You a Wine Sommelier. In The Art of Structuring. https://doi.org/10.1007/978-3-030-06234-7_29
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| DOI
2019 | Preprint | LibreCat-ID: 16341 |
Brandt, S., Jähn, C., Fischer, M., & Meyer auf der Heide, F. (2019). Rendering of Complex Heterogenous Scenes using Progressive Blue Surfels. ArXiv:1904.08225.
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| arXiv
2019 | Mastersthesis | LibreCat-ID: 14546
Hansmeier, T. (2019). Autonomous Operation of High-Performance Compute Nodes through Self-Awareness and Learning Classifiers. Universität Paderborn.
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2019 | Conference Paper | LibreCat-ID: 13107
Schumacher, J., & Rezat, S. (n.d.). A Hypothetical Learning Trajectory for the Learning of the Rules for Manipulating Integers. In U. T. Jankvist, M. Van den Heuvel-Panhuizen, & M. Veldhuis (Eds.), Proceedings of the Eleventh Congress of the European Society for Research in Mathematics Education (CERME11, February 6 – 10, 2019). Utrecht, the Netherlands: Freudenthal Group & Freudenthal Institute, Utrecht University and ERME.
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2019 | Conference Abstract | LibreCat-ID: 13132
Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.
LibreCat
2019 | Conference Paper | LibreCat-ID: 13250
Ansótegui, C., Heymann, B., Pon, J., Sellmann, M., & Tierney, K. (2019). Hyper-Reactive Tabu Search for MaxSAT. In Learning and Intelligent Optimization (pp. 309–325). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-05348-2_27
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| DOI
2019 | Conference Paper | LibreCat-ID: 13271 |
von Neumann, T., Kinoshita, K., Delcroix, M., Araki, S., Nakatani, T., & Haeb-Umbach, R. (2019). All-neural Online Source Separation, Counting, and Diarization for Meeting Analysis. In ICASSP 2019, Brighton, UK.
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2019 | Conference Paper | LibreCat-ID: 13442
Manavi, S. A., & Kenig, E. (2019). Numerical Simulation of Forced Convection in a Microchannel with Realistic Roughness of 3D Printed Surface. In Computer Aided Chemical Engineering. Eindhoven, Netherlands: 29th European Symposium on Computer Aided Process Engineering. https://doi.org/10.1016/b978-0-12-818634-3.50138-7
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| DOI
2019 | Conference Paper | LibreCat-ID: 13443 |
Redder, A., Ramaswamy, A., & Quevedo, D. (2019). Deep reinforcement learning for scheduling in large-scale networked control systems. In Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems. Chicago, USA.
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