| Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data |
Ábel Elekes, Adrian Englhardt, Martin Schäler, Klemens Böhm |
22nd Conference on Computational Natural Language Learning (CoNLL 2018). DOI: 10.18653/v1/K18-1041 |
| Towards More Meaningful Notions of Similarity in NLP Embedding Models |
Ábel Elekes, Adrian Englhardt, Martin Schäler, Klemens Böhm |
International Journal on Digital Libraries (2020). DOI: 10.1007/s00799-018-0237-y |
| Efficient SVDD sampling with approximation guarantees for the decision boundary |
Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm |
Machine Learning Journal, 2022 |
| Finding the Sweet Spot: Batch Selection for One-Class Active Learning |
Adrian Englhardt, Holger Trittenbach, Dennis Vetter, Klemens Böhm |
2020 SIAM International Conference on Data Mining (SDM 2020). DOI: 10.1137/1.9781611976236.14 |
| Improving Semantic Change Analysis by Combining Word Embeddings and Word Frequencies |
Adrian Englhardt, Jens Willkomm, Martin Schäler, Klemens Böhm |
International Journal on Digital Libraries (2020). DOI: 10.1007/s00799-019-00271-6 |
| Exploring the Unknown - Query Synthesis in One-Class Active Learning |
Adrian Englhardt, Klemens Böhm |
2020 SIAM International Conference on Data Mining (SDM 2020). DOI: 10.1137/1.9781611976236.17 |
| An Evaluation of Combinations of Lossy Compression and Change-Detection Approaches for Time-Series Data |
Gregor Hollmig, Matthias Horne, Simon Leimkühler, Frederik Schöll, Carsten Strunk, Adrian Englhardt, Pavel Efros, Erik Buchmann, Klemens Böhm |
Information Systems |
| An overview and a benchmark of active learning for outlier detection with one-class classifiers |
Holger Trittenbach, Adrian Englhardt, Klemens Böhm |
Expert Systems with Applications (2021). DOI: 10.1016/j.eswa.2020.114372 |
| Validating One-Class Active Learning with User Studies - a Prototype and Open Challenges |
Holger Trittenbach, Adrian Englhardt, Klemens Böhm |
3rd International Workshop on Interactive Adaptive Learning (IAL 2019). URL: https://ceur-ws.org/Vol-2444/ialatecml_paper2.pdf
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| Energy Time-Series Features for Emerging Applications on the Basis of Human-Readable Machine Descriptions |
Michael Vollmer, Holger Trittenbach, Shahab Karrari, Adrian Englhardt, Pawel Bielski, Klemens Böhm |
2nd International Workshop on Energy Data and Analytics (EDA 2019). DOI: 10.1145/3307772.3331022 |
| How to Quantify the Impact of Lossy Transformations on Change Detection |
Pavel Efros, Erik Buchmann, Adrian Englhardt, Klemens Böhm |
Proceedings of 27th International Conference on Scientific and Statistical Database Management (SSDBM 2015 ), San Diego, CA, USA |
| How to Quantify the Impact of Lossy Transformations on Event Detection |
Pavel Efros, Erik Buchmann, Adrian Englhardt, Klemens Böhm |
Big Data Research, special issue on Online Forecasting and Proactive Analytics in the Big Data Era, Elsevier Science Publisher, 2017 |
