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7 Publications
2023 | Conference Paper | LibreCat-ID: 45695 | 
Hotegni, S. S., Mahabadi, S., & Vakilian, A. (n.d.). Approximation Algorithms for Fair Range Clustering. Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023. International Conference on Machine Learning, Honolulu, Hawaii, USA.
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2022 | Conference Paper | LibreCat-ID: 29934
Muth, L., Noll, C., & Sextro, W. (2022). Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data. In A. Orlova & D. Cole (Eds.), Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021. Springer. https://doi.org/10.1007/978-3-031-07305-2_92
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2021 | Conference Paper | LibreCat-ID: 27111 |
Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., & Sextro, W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, & O. Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021 (Vol. 6, Issue 1, pp. 527–536). https://doi.org/10.36001/phme.2021.v6i1.3042
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2016 | Conference Paper | LibreCat-ID: 2367
Blömer, J., Brauer, S., & Bujna, K. (2016). A Theoretical Analysis of the Fuzzy K-Means Problem. In 2016 IEEE 16th International Conference on Data Mining (ICDM) (pp. 805–810). Barcelona, Spain: IEEE. https://doi.org/10.1109/icdm.2016.0094
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2010 | Journal Article | LibreCat-ID: 2990
Ackermann, M. R., Blömer, J., & Sohler, C. (2010). Clustering for Metric and Nonmetric Distance Measures. ACM Trans. Algorithms, (4), 59:1--59:26. https://doi.org/10.1145/1824777.1824779
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2007 | Conference Paper | LibreCat-ID: 6508
Kaufmann, P., & Platzner, M. (2007). MOVES: A Modular Framework for Hardware Evolution. In Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007) (pp. 447–454). Edinburgh, UK: IEEE. https://doi.org/10.1109/ahs.2007.73
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2001 | Journal Article | LibreCat-ID: 11778 |
Haeb-Umbach, R. (2001). Automatic generation of phonetic regression class trees for MLLR adaptation. IEEE Transactions on Speech and Audio Processing, 9(3), 299–302. https://doi.org/10.1109/89.906003
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