| Large scale multiple kernel learning S Sonnenburg, G Rätsch, C Schäfer, B Schölkopf The Journal of Machine Learning Research 7, 1531-1565, 2006 | 1739 | 2006 |
| Learning intrusion detection: supervised or unsupervised? P Laskov, P Düssel, C Schäfer, K Rieck International Conference on Image Analysis and Processing, 50-57, 2005 | 379 | 2005 |
| Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis B Blankertz, G Dornhege, C Schafer, R Krepki, J Kohlmorgen, KR Muller, ... IEEE Transactions on Neural Systems and Rehabilitation Engineering 11 (2 …, 2003 | 337 | 2003 |
| Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis B Blankertz, G Dornhege, C Schäfer, R Krepki, J Kohlmorgen, KR Müller, ... Neural Systems and Rehabilitation Engineering, IEEE Transactions on 11 (2 …, 2003 | 337 | 2003 |
| BCI competition 2003-data set III: probabilistic modeling of sensorimotor μ rhythms for classification of imaginary hand movements S Lemm, C Schäfer, G Curio Biomedical Engineering, IEEE Transactions on 51 (6), 1077-1080, 2004 | 292 | 2004 |
| A general and efficient multiple kernel learning algorithm S Sonnenburg, G Rätsch, C Schäfer Advances in neural information processing systems 18, 1273-1280, 2005 | 234 | 2005 |
| Intrusion detection in unlabeled data with quarter-sphere support vector machines P Laskov, C Schäfer, I Kotenko, KR Müller Praxis der Informationsverarbeitung und Kommunikation 27 (4), 228-236, 2004 | 141 | 2004 |
| Learning interpretable SVMs for biological sequence classification G Rätsch, S Sonnenburg, C Schäfer BMC bioinformatics 7, 1-14, 2006 | 103 | 2006 |
| Single trial detection of EEG error potentials: A tool for increasing BCI transmission rates B Blankertz, C Schäfer, G Dornhege, G Curio Artificial Neural Networks—ICANN 2002: International Conference Madrid …, 2002 | 71 | 2002 |
| Learning interpretable SVMs for biological sequence classification S Sonnenburg, G Rätsch, C Schäfer Annual International Conference on Research in Computational Molecular …, 2005 | 70 | 2005 |
| Optimal dyadic decision trees G Blanchard, C Schäfer, Y Rozenholc, KR Müller Machine Learning 66 (2-3), 209-241, 2007 | 67 | 2007 |
| Automatic identification of faked and fraudulent interviews in the German SOEP C Schaefer, JP Schräpler, KR Müller, GG Wagner Schmollers Jahrbuch: Journal of Applied Social Science Studies/Zeitschrift …, 2005 | 53 | 2005 |
| Visualization of anomaly detection using prediction sensitivity P Laskov, K Rieck, C Schäfer, KR Müller Sicherheit 2005, Sicherheit–Schutz und Zuverlässigkeit, 2005 | 47 | 2005 |
| Oracle bounds and exact algorithm for dyadic classification trees G Blanchard, C Schäfer, Y Rozenholc International Conference on Computational Learning Theory, 378-392, 2004 | 42 | 2004 |
| Automatic identification of faked and fraudulent interviews in surveys by two different methods C Schäfer, JP Schräpler, KR Müller, GG Wagner ASA Section on Survey Research Methods, 4318-4325, 2004 | 38 | 2004 |
| Method and Apparatus for Automatic Online Detection and Classification of Anomalous Objects in a Data Stream KR Muller, P Laskov, D Tax, C Schafer US Patent App. 10/568,217, 2008 | 23 | 2008 |
| Aggregating classification accuracy across time: Application to single trial EEG S Lemm, C Schäfer, G Curio Advances in Neural Information Processing Systems, 825-832, 2006 | 14 | 2006 |
| Support vector machines S Mika, C Schäfer, P Laskov, D Tax, KR Müller Handbook of Computational Statistics: Concepts and Methods, 841-876, 2004 | 10 | 2004 |
| Support vector machines K Rieck, S Sonnenburg, S Mika, C Schäfer, P Laskov, D Tax, KR Müller Handbook of Computational Statistics: Concepts and Methods, 883-926, 2012 | 9 | 2012 |
| Determinants of fertility C Schäfer, C Schmitt, C Schmitt Duncker & Humblot (Berlin), 2007 | 9* | 2007 |
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, Germany在 tu-berlin.de 的电子邮件经过验证
