| Multi-view clustering. S Bickel, T Scheffer ICDM 4 (2004), 19-26, 2004 | 692 | 2004 |
| Active hidden markov models for information extraction T Scheffer, C Decomain, S Wrobel International Symposium on Intelligent Data Analysis, 309-318, 2001 | 399 | 2001 |
| Discriminative learning for differing training and test distributions S Bickel, M Brückner, T Scheffer Proceedings of the 24th international conference on Machine learning, 81-88, 2007 | 383 | 2007 |
| Unsupervised prediction of citation influences L Dietz, S Bickel, T Scheffer Proceedings of the 24th international conference on Machine learning, 233-240, 2007 | 311 | 2007 |
| Discriminative learning under covariate shift. S Bickel, M Brückner, T Scheffer Journal of Machine Learning Research 10 (9), 2009 | 277 | 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 | 203 | 2006 |
| Co-EM support vector learning U Brefeld, T Scheffer Proceedings of the twenty-first international conference on Machine learning, 16, 2004 | 203 | 2004 |
| Finding association rules that trade support optimally against confidence T Scheffer European conference on principles of data mining and knowledge discovery …, 2001 | 192 | 2001 |
| Multi-task learning for HIV therapy screening S Bickel, J Bogojeska, T Lengauer, T Scheffer Proceedings of the 25th international conference on Machine learning, 56-63, 2008 | 183 | 2008 |
| Stackelberg games for adversarial prediction problems M Brückner, T Scheffer Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011 | 176 | 2011 |
| AUC maximizing support vector learning U Brefeld, T Scheffer Proceedings of the ICML 2005 workshop on ROC Analysis in Machine Learning, 2005 | 169 | 2005 |
| Static prediction games for adversarial learning problems M Brückner, C Kanzow, T Scheffer The Journal of Machine Learning Research 13 (1), 2617-2654, 2012 | 163 | 2012 |
| Taxonomic metagenome sequence assignment with structured output models KR Patil, P Haider, PB Pope, PJ Turnbaugh, M Morrison, T Scheffer, ... Nature methods 8 (3), 191-192, 2011 | 127 | 2011 |
| Thwarting the nigritude ultramarine: Learning to identify link spam I Drost, T Scheffer European Conference on Machine Learning, 96-107, 2005 | 110 | 2005 |
| Dirichlet-enhanced spam filtering based on biased samples S Bickel, T Scheffer Advances in neural information processing systems, 161-168, 2007 | 101 | 2007 |
| Finding association rules that trade support optimally against confidence T Scheffer Intelligent Data Analysis 9 (4), 381-395, 2005 | 101 | 2005 |
| Semi-supervised learning for structured output variables U Brefeld, T Scheffer Proceedings of the 23rd international conference on Machine learning, 145-152, 2006 | 93 | 2006 |
| Finding the most interesting patterns in a database quickly by using sequential sampling T Scheffer, S Wrobel Journal of Machine Learning Research 3 (Dec), 833-862, 2002 | 92 | 2002 |
| Algebraic foundation and improved methods of induction of ripple down rules T Scheffer In, 1996 | 82 | 1996 |
| Multi-relational learning, text mining, and semi-supervised learning for functional genomics MA Krogel, T Scheffer Machine Learning 57 (1-2), 61-81, 2004 | 76 | 2004 |
Steffen BickelNokia Gate5 GmbHVerified email at cs.uni-potsdam.de
