| Toward supervised anomaly detection N Görnitz, M Kloft, K Rieck, U Brefeld Journal of Artificial Intelligence Research 46, 235-262, 2013 | 504 | 2013 |
| lp-Norm Multiple Kernel Learning M Kloft, U Brefeld, S Sonnenburg, A Zien The Journal of Machine Learning Research 12, 953-997, 2011 | 466 | 2011 |
| Efficient and accurate lp-norm multiple kernel learning M Kloft, U Brefeld, P Laskov, KR Müller, A Zien, S Sonnenburg Advances in neural information processing systems 22, 2009 | 358 | 2009 |
| Efficient co-regularised least squares regression U Brefeld, T Gärtner, T Scheffer, S Wrobel Proceedings of the 23rd international conference on Machine learning, 137-144, 2006 | 235 | 2006 |
| Co-EM support vector learning U Brefeld, T Scheffer Proceedings of the twenty-first international conference on Machine learning, 16, 2004 | 228 | 2004 |
| AUC maximizing support vector learning U Brefeld, T Scheffer Proceedings of the ICML 2005 workshop on ROC Analysis in Machine Learning, 2005 | 192 | 2005 |
| Non-sparse regularization and efficient training with multiple kernels M Kloft, U Brefeld, S Sonnenburg, A Zien arXiv preprint arXiv:1003.0079 186, 189-190, 2010 | 156 | 2010 |
| Support vector machines with example dependent costs U Brefeld, P Geibel, F Wysotzki Machine Learning: ECML 2003: 14th European Conference on Machine Learning …, 2003 | 108 | 2003 |
| Semi-supervised learning for structured output variables U Brefeld, T Scheffer Proceedings of the 23rd international conference on Machine learning, 145-152, 2006 | 102 | 2006 |
| Active learning for network intrusion detection N Görnitz, M Kloft, K Rieck, U Brefeld Proceedings of the 2nd ACM workshop on Security and artificial intelligence …, 2009 | 96 | 2009 |
| Factored MDPs for detecting topics of user sessions M Tavakol, U Brefeld Proceedings of the 8th ACM Conference on Recommender Systems, 33-40, 2014 | 87 | 2014 |
| Automatic feature selection for anomaly detection M Kloft, U Brefeld, P Düessel, C Gehl, P Laskov Proceedings of the 1st ACM workshop on Workshop on AISec, 71-76, 2008 | 73 | 2008 |
| Multi-view discriminative sequential learning U Brefeld, C Büscher, T Scheffer Machine Learning: ECML 2005: 16th European Conference on Machine Learning …, 2005 | 72 | 2005 |
| Spatio-temporal convolution kernels K Knauf, D Memmert, U Brefeld Machine learning 102, 247-273, 2016 | 59 | 2016 |
| Hybrid models for future event prediction G Amodeo, R Blanco, U Brefeld Proceedings of the 20th ACM international conference on Information and …, 2011 | 57 | 2011 |
| Learning to rate player positioning in soccer U Dick, U Brefeld Big data 7 (1), 71-82, 2019 | 55 | 2019 |
| Transductive support vector machines for structured variables A Zien, U Brefeld, T Scheffer Proceedings of the 24th international conference on Machine learning, 1183-1190, 2007 | 54 | 2007 |
| Probabilistic movement models and zones of control U Brefeld, J Lasek, S Mair Machine Learning 108 (1), 127-147, 2019 | 51 | 2019 |
| Systematic feature evaluation for gene name recognition J Hakenberg, S Bickel, C Plake, U Brefeld, H Zahn, L Faulstich, U Leser, ... BMC bioinformatics 6, 1-11, 2005 | 51 | 2005 |
| Predicting the difficulty of exercise items for dynamic difficulty adaptation in adaptive language tutoring I Pandarova, T Schmidt, J Hartig, A Boubekki, RD Jones, U Brefeld International Journal of Artificial Intelligence in Education 29, 342-367, 2019 | 50 | 2019 |
Marius KloftProfessor, RPTU Kaiserslautern-Landau在 cs.uni-kl.de 的电子邮件经过验证
