| Logistic model trees N Landwehr, M Hall, E Frank Machine learning 59, 161-205, 2005 | 1787 | 2005 |
| kFOIL: Learning simple relational kernels N Landwehr, A Passerini, L De Raedt, P Frasconi Aaai 6, 389-394, 2006 | 137 | 2006 |
| The future agricultural biogas plant in Germany: A vision S Theuerl, C Herrmann, M Heiermann, P Grundmann, N Landwehr, ... Energies 12 (3), 396, 2019 | 130 | 2019 |
| nFOIL: Integrating naıve bayes and FOIL N Landwehr, K Kersting, L De Raedt Proceedings of the twentieth national conference on artificial intelligence …, 2005 | 114 | 2005 |
| A nonergodic ground‐motion model for California with spatially varying coefficients N Landwehr, NM Kuehn, T Scheffer, N Abrahamson Bulletin of the Seismological Society of America 106 (6), 2574-2583, 2016 | 105 | 2016 |
| Integrating naive bayes and FOIL. N Landwehr, K Kersting, L De Raedt Journal of Machine Learning Research 8 (3), 2007 | 98 | 2007 |
| From face to face: the contribution of facial mimicry to cognitive and emotional empathy H Drimalla, N Landwehr, U Hess, I Dziobek Cognition and Emotion, 2019 | 84 | 2019 |
| Probabilistic seismic hazard analysis in California using nonergodic ground‐motion models NA Abrahamson, NM Kuehn, M Walling, N Landwehr Bulletin of the Seismological Society of America 109 (4), 1235-1249, 2019 | 55 | 2019 |
| Towards digesting the alphabet-soup of statistical relational learning L De Raedt, B Demoen, D Fierens, B Gutmann, G Janssens, A Kimmig, ... NIPS* 2008 Workshop Probabilistic Programming, Date: 2008/12/13-2008/12/13 …, 2008 | 54 | 2008 |
| Active risk estimation C Sawade, N Landwehr, S Bickel, T Scheffer Proceedings of the 27th International Conference on Machine Learning (ICML …, 2010 | 53 | 2010 |
| Stochastic relational processes: Efficient inference and applications I Thon, N Landwehr, L De Raedt Machine Learning 82, 239-272, 2011 | 51 | 2011 |
| Fast learning of relational kernels N Landwehr, A Passerini, L De Raedt, P Frasconi Machine learning 78, 305-342, 2010 | 48 | 2010 |
| Relational transformation-based tagging for activity recognition N Landwehr, B Gutmann, I Thon, L De Raedt, M Philipose Fundamenta Informaticae 89 (1), 111-129, 2008 | 46 | 2008 |
| Modeling interleaved hidden processes N Landwehr Proceedings of the 25th international conference on Machine learning, 520-527, 2008 | 39 | 2008 |
| Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks H Tavakoli, P Alirezazadeh, A Hedayatipour, AHB Nasib, N Landwehr Computers and electronics in agriculture 181, 105935, 2021 | 37 | 2021 |
| A simple model for sequences of relational state descriptions I Thon, N Landwehr, L De Raedt Machine Learning and Knowledge Discovery in Databases: European Conference …, 2008 | 34 | 2008 |
| Towards the automatic detection of social biomarkers in autism spectrum disorder: Introducing the simulated interaction task (SIT) H Drimalla, T Scheffer, N Landwehr, I Baskow, S Roepke, B Behnia, ... NPJ digital medicine 3 (1), 25, 2020 | 29 | 2020 |
| Boosting relational sequence alignments A Karwath, K Kersting, N Landwehr 2008 Eighth IEEE International Conference on Data Mining, 857-862, 2008 | 28 | 2008 |
| Early detection of stripe rust in winter wheat using deep residual neural networks M Schirrmann, N Landwehr, A Giebel, A Garz, KH Dammer Frontiers in Plant Science 12, 469689, 2021 | 27 | 2021 |
| Varying-coefficient models for geospatial transfer learning M Bussas, C Sawade, N Kühn, T Scheffer, N Landwehr Machine Learning 106, 1419-1440, 2017 | 26 | 2017 |
