Trittenbach

Dr.-Ing. Holger Trittenbach

  • Lehrstuhl
    Prof. K. Böhm

    Karlsruher Institut für Technologie
    Am Fasanengarten 5
    76131 Karlsruhe
    GERMANY   

Publikationen


2021
An overview and a benchmark of active learning for outlier detection with one-class classifiers.
Trittenbach, H.; Englhardt, A.; Böhm, K.
2021. Expert systems with applications, 168, Art. Nr.: 114372. doi:10.1016/j.eswa.2020.114372
2020
Active Learning of SVDD Hyperparameter Values.
Trittenbach, H.; Böhm, K.; Assent, I.
2020. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics: 6-9 Ocotber 2020, Sydney, Australia. Ed.: G. Webb, 109–117, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/DSAA49011.2020.00023
Finding the sweet spot: Batch selection for one-class active learning.
Englhardt, A.; Trittenbach, H.; Vetter, D.; Böhm, K.
2020. Proceedings of the 2020 SIAM International Conference on Data Mining. Ed.: C. Demeniconi, 118–126, SIAM. doi:10.1137/1.9781611976236.14
2019
One-class active learning for outlier detection with multiple subspaces.
Trittenbach, H.; Böhm, K.
2019. CIKM ’19 Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China, November 3-7, 2019, 811–820, Association for Computing Machinery (ACM). doi:10.1145/3357384.3357873
Validating one-class active learning with user studies – A prototype and open challenges.
Trittenbach, H.; Englhardt, A.; Böhm, K.
2019. IAL 2019 Interactive Adaptive Learning: Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019), Würzburg, Germany, September 16th, 2019. Ed.: D. Kottke, 17–31 VolltextVolltext der Publikation als PDF-Dokument
Data-driven crack assessment based on surface measurements.
Schulz, K.; Kreis, S.; Trittenbach, H.; Böhm, K.
2019. Engineering fracture mechanics, 218, Article no: 106552. doi:10.1016/j.engfracmech.2019.106552
The Effect of Temporal Aggregation on Battery Sizing for Peak Shaving.
Werle, D.; Warzel, D.; Bischof, S.; Koziolek, A.; Trittenbach, H.; Böhm, K.
2019. Tenth ACM International Conference on Future Energy Systems (ACM e-Energy) and its co-located workshops, Phoenix, AZ, United States, 25th - 28th of June 2019, 482–485, Association for Computing Machinery (ACM). doi:10.1145/3307772.3331023
Energy Time-Series Features for Emerging Applications on the Basis of Human-Readable Machine Descriptions.
Vollmer, M.; Englhardt, A.; Trittenbach, H.; Bielski, P.; Karrari, S.; Böhm, K.
2019. Tenth ACM International Conference on Future Energy Systems (ACM e-Energy) and its co-located workshops, Phoenix, AZ, United States, 25th - 28th of June 2019, 474–481, Association for Computing Machinery (ACM). doi:10.1145/3307772.3331022
2018
Towards Simulation-Data Science : A Case Study on Material Failures.
Trittenbach, H.; Gauch, M.; Böhm, K.; Schulz, K.
2018. IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, 1-3 Oct. 2018, 450–459, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/DSAA.2018.00058
An Overview and a Benchmark of Active Learning for One-Class Classification.
Trittenbach, H.; Englhardt, A.; Böhm, K.
2018. arXiv preprint 1808.04759 
HIPE – An energy-Status-Data set from industrial production.
Bischof, S.; Trittenbach, H.; Vollmer, M.; Werle, D.; Blank, T.; Böhm, K.
2018. 9th ACM International Conference on Future Energy Systems, e-Energy 2018; Karlsruhe; Germany; 12 June 2018 through 15 June 2018, 599–603, Association for Computing Machinery (ACM). doi:10.1145/3208903.3210278
On the tradeoff between energy data aggregation and clustering quality.
Trittenbach, H.; Bach, J.; Böhm, K.
2018. 9th ACM International Conference on Future Energy Systems, e-Energy 2018; Karlsruhe; Germany; 12 June 2018 through 15 June 2018, 399–401, Association for Computing Machinery (ACM). doi:10.1145/3208903.3212038
Dimension-based subspace search for outlier detection.
Trittenbach, H.; Böhm, K.
2018. International Journal of Data Science and Analytics. doi:10.1007/s41060-018-0137-7
Lehrveranstaltungen
Titel Typ Semester
Vorlesung (V) WS 18/19
Praktikum (P) SS 2017