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Books and Theses
- 2021
[b1]Adrian Englhardt:
Cost-Quality Trade-Offs in One-Class Active Learning. Karlsruhe Institute of Technology, Germany, 2021
Journal Articles
- 2022
- [j6]Adrian Englhardt
, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm:
Efficient SVDD sampling with approximation guarantees for the decision boundary. Mach. Learn. 111(4): 1349-1375 (2022) - 2021
- [j5]Holger Trittenbach, Adrian Englhardt, Klemens Böhm:
An overview and a benchmark of active learning for outlier detection with one-class classifiers. Expert Syst. Appl. 168: 114372 (2021) - 2020
- [j4]Ábel Elekes, Adrian Englhardt, Martin Schäler, Klemens Böhm:
Toward meaningful notions of similarity in NLP embedding models. Int. J. Digit. Libr. 21(2): 109-128 (2020) - [j3]Adrian Englhardt, Jens Willkomm, Martin Schäler, Klemens Böhm:
Improving semantic change analysis by combining word embeddings and word frequencies. Int. J. Digit. Libr. 21(3): 247-264 (2020) - 2017
- [j2]Pavel Efros, Erik Buchmann, Adrian Englhardt, Klemens Böhm:
How to Quantify the Impact of Lossy Transformations on Event Detection. Big Data Res. 9: 84-97 (2017) - [j1]Gregor Hollmig, Matthias Horne, Simon Leimkühler, Frederik Schöll, Carsten Strunk, Adrian Englhardt, Pavel Efros, Erik Buchmann, Klemens Böhm:
An evaluation of combinations of lossy compression and change-detection approaches for time-series data. Inf. Syst. 65: 65-77 (2017)
Conference and Workshop Papers
- 2020
- [c5]Adrian Englhardt, Holger Trittenbach, Dennis Vetter, Klemens Böhm:
Finding the Sweet Spot: Batch Selection for One-Class Active Learning. SDM 2020: 118-126 - [c4]Adrian Englhardt, Klemens Böhm:
Exploring the Unknown - Query Synthesis in One-Class Active Learning. SDM 2020: 145-153 - 2019
- [c3]Michael Vollmer, Adrian Englhardt, Holger Trittenbach, Pawel Bielski, Shahab Karrari, Klemens Böhm:
Energy Time-Series Features for Emerging Applications on the Basis of Human-Readable Machine Descriptions. e-Energy 2019: 474-481 - 2018
- [c2]Ábel Elekes, Adrian Englhardt, Martin Schäler, Klemens Böhm:
Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data. CoNLL 2018: 423-432 - 2015
- [c1]Pavel Efros, Erik Buchmann, Adrian Englhardt, Klemens Böhm:
How to quantify the impact of lossy transformations on change detection. SSDBM 2015: 17:1-17:11
Informal and Other Publications
- 2020
- [i2]Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm:
Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary. CoRR abs/2009.13853 (2020) - 2018
- [i1]Holger Trittenbach, Adrian Englhardt, Klemens Böhm:
An Overview and a Benchmark of Active Learning for One-Class Classification. CoRR abs/1808.04759 (2018)
Coauthor Index
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