| Interpreting PET scans by structured patient data: a data mining case study in dementia research J Schmidt, A Hapfelmeier, M Mueller, R Perneczky, A Kurz, A Drzezga, ... Knowledge and Information Systems 24, 149-170, 2010 | 37 | 2010 |
| Inductive databases in the relational model: The data as the bridge S Kramer, V Aufschild, A Hapfelmeier, A Jarasch, K Kessler, S Reckow, ... Knowledge Discovery in Inductive Databases: 4th International Workshop, KDID …, 2006 | 28 | 2006 |
| A case study of stacked multi-view learning in dementia research R Li, A Hapfelmeier, J Schmidt, R Perneczky, A Drzezga, A Kurz, ... Artificial Intelligence in Medicine: 13th Conference on Artificial …, 2011 | 14 | 2011 |
| Fast mutual information computation for dependency-monitoring on data streams J Boidol, A Hapfelmeier Proceedings of the Symposium on Applied Computing, 830-835, 2017 | 10 | 2017 |
| Pruning incremental linear model trees with approximate lookahead A Hapfelmeier, B Pfahringer, S Kramer IEEE Transactions on Knowledge and Data Engineering 26 (8), 2072-2076, 2013 | 10 | 2013 |
| Learning probabilistic real-time automata from multi-attribute event logs J Schmidt, A Ghorbani, A Hapfelmeier, S Kramer Intelligent Data Analysis 17 (1), 93-123, 2013 | 8 | 2013 |
| Towards real-time machine learning A Hapfelmeier, C Mertes, J Schmidt, S Kramer Proceedings of the ECML PKDD 2012 Workshop on Instant Interactive Data Mining, 2012 | 7 | 2012 |
| Low postoperative platelet count is associated with higher morbidity after liver surgery for colorectal metastases C Riediger, J Bachmann, A Hapfelmeier, J Kleeff, H Friess, MW Mueller J Liver 3 (4), 166, 2014 | 5 | 2014 |
| Improving wound score classification with limited remission spectra J Schmidt, A Hapfelmeier, WD Schmidt, U Wollina International Wound Journal 9 (2), 189-198, 2012 | 4 | 2012 |
| Detecting data stream dependencies on high dimensional data J Boidol, A Hapfelmeier International Conference on Internet of Things and Big Data 2, 383-390, 2016 | 3 | 2016 |
| Big data k-anonymizing by parallel semantic micro-aggregation A Hapfelmeier, M Imig, M Mock US Patent 11,615,209, 2023 | 2 | 2023 |
| AI Hazard Management: A framework for the systematic management of root causes for AI risks R Schnitzer, A Hapfelmeier, S Gaube, S Zillner International Conference on Frontiers of Artificial Intelligence, Ethics …, 2023 | 1 | 2023 |
| Probabilistic Hoeffding Trees: Sped-Up Convergence and Adaption of Online Trees on Changing Data Streams J Boidol, A Hapfelmeier, V Tresp Advances in Data Mining: Applications and Theoretical Aspects: 15th …, 2015 | 1 | 2015 |
| Incremental linear model trees on massive datasets: keep it simple, keep it fast A Hapfelmeier, J Schmidt, S Kramer Proceedings of the 28th Annual ACM Symposium on Applied Computing, 129-135, 2013 | 1 | 2013 |
| Entropy-based validation of sensor measurements JC Boidol, A Hapfelmeier US Patent 11,403,478, 2022 | | 2022 |
| Method and system for anonymising data stocks A Hapfelmeier, M Imig, M Mock US Patent 11,244,073, 2022 | | 2022 |
| Determining an Investment Solution for at Least One Investment Area of A Region A Hapfelmeier, SH Weber US Patent App. 14/584,707, 2016 | | 2016 |
| Incremental Linear Model Trees on Big Data A Hapfelmeier Technische Universität München, 2016 | | 2016 |
| [18F] FDG PET-based clustering of patients with cognitive deficits: A data-mining approach. R Perneczky, A Hapfelmeier, J Schmidt, M Mueller, M Wermke, A Kurz, ... EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 35, S312-S312, 2008 | | 2008 |
