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Note: Links lead to the DBLP on the Web. Tobias Scheffer Steffen Bickel , Tobias Scheffer: Learning from Message Pairs for Automatic Email Answering. ECML 2004 : 87-98 Steffen Bickel , Tobias Scheffer: Multi-View Clustering. ICDM 2004 : 19-26 Ulf Brefeld , Tobias Scheffer: Co-EM support vector learning. ICML 2004 Korinna Grabski , Tobias Scheffer: Sentence completion. SIGIR 2004 : 433-439 Mark-A. Krogel , Tobias Scheffer: Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics. Machine Learning 57 (1-2): 61-81 (2004) Mark-A. Krogel , Tobias Scheffer: Effectiveness of information extraction, multi-relational, and multi-view learning for prediction gene deletion experiments. BIOKDD 2003 : 10-16 Mark-A. Krogel , Tobias Scheffer: Effectiveness of Information Extraction, Multi-Relational, and Semi-Supervised Learning for Predicting Functional Properties of Genes. ICDM 2003 : 569-572 Michael Kockelkorn , Andreas Lüneburg , Tobias Scheffer: Learning to Answer Emails. IDA 2003 : 25-35 Michael Kockelkorn , Andreas Lüneburg , Tobias Scheffer: Using Transduction and Multi-view Learning to Answer Emails. PKDD 2003 : 266-277 Tobias Scheffer, Stefan Wrobel : A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases. PKDD 2002 : 397-409 Tobias Scheffer, Stefan Wrobel : Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling. Journal of Machine Learning Research 3 : 833-862 (2002) Tobias Scheffer, Stefan Wrobel , Borislav Popov , Damyan Ognianov , Christian Decomain , Susanne Hoche : Lerning Hidden Markov Models for Information Extraction Actively from Partially Labeled Text. KI 16 (2): 17-22 (2002) Mark-A. Krogel , Marcus Denecke , Marco Landwehr , Tobias Scheffer: Combining Data and Text Mining Techniques for Yeast Gene Regulation Prediction: A Case Study. SIGKDD Explorations 4 (2): 104-105 (2002) Hans Gründel , Tino Naphtali , Christian Wiech , Jan-Marian Gluba , Maiken Rohdenburg , Tobias Scheffer: Clipping and Analyzing News Using Machine Learning Techniques. Discovery Science 2001 : 87-99 Tobias Scheffer, Christian Decomain , Stefan Wrobel : Mining the Web with Active Hidden Markov Models. ICDM 2001 : 645-646 Tobias Scheffer, Stefan Wrobel : Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems. ICML 2001 : 481-488 Tobias Scheffer, Christian Decomain , Stefan Wrobel : Active Hidden Markov Models for Information Extraction. IDA 2001 : 309-318 Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence. PKDD 2001 : 424-435 Tobias Scheffer: Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees. ALT 2000 : 194-208 Tobias Scheffer: Nonparametric Regularization of Decision Trees. ECML 2000 : 344-356 Tobias Scheffer: Predicting the Generalization Performance of Cross Validatory Model Selection Criteria. ICML 2000 : 831-838 Tobias Scheffer, Stefan Wrobel : A sequential sampling algorithm for a general class of utility criteria. KDD 2000 : 330-334 Andrew R. Mitchell , Tobias Scheffer, Arun Sharma , Frank Stephan : The VC-Dimension of Subclasses of Pattern. ATL 1999 : 93-105 Tobias Scheffer, Thorsten Joachims : Expected Error Analysis for Model Selection. ICML 1999 : 361-370 Tobias Scheffer: Error Estimation and Model Selection. KI 13 (3): 46-48 (1999) Tobias Scheffer: International Conference on Machine Learning (ICML-99). KI 13 (4): 68 (1999) Tobias Scheffer, Thorsten Joachims : Estimating the Expected Error of Empirical Minimizers for Model Selection. AAAI/IAAI 1998 : 1200 Tobias Scheffer, Russell Greiner , Christian Darken : Why Experimentation can be better than "Perfect Guidance". ICML 1997 : 331-339 Tobias Scheffer, Ralf Herbrich : Unbiased Assesment of Learning Algorithms. IJCAI (2) 1997 : 798-803 Tobias Scheffer, Ralf Herbrich , Fritz Wysotzki : Efficient Theta-Subsumption Based on Graph Algorithms. Inductive Logic Programming Workshop 1996 : 212-228 Tobias Scheffer: A Generic Algorithm for Learning Rules with Hierarchical Exceptions. SBIA 1995 : 181-190 1 [ 30 ] [ 31 ] 2 [ 29 ] 3 [ 4 ] 4 [ 15 ] [ 17 ] [ 20 ] 5 [ 19 ] 6 [ 18 ] 7 [ 28 ] 8 [ 4 ] 9 [ 18 ] 10 [ 2 ] [ 3 ] 11 [ 20 ] 12 [ 5 ] [ 8 ] 13 [ 23 ] [ 24 ] 14 [ 19 ] [ 25 ] [ 26 ] [ 27 ] 15 [ 19 ] 16 [ 23 ] [ 24 ] 17 [ 9 ] 18 [ 18 ] 19 [ 20 ] 20 [ 20 ] 21 [ 18 ] 22 [ 9 ] 23 [ 9 ] 24 [ 18 ] 25 [ 10 ] [ 15 ] [ 16 ] [ 17 ] [ 20 ] [ 21 ] [ 22 ] 26 [ 2 ]  ©2005 Association for Computing Machinery |
