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Tobias Scheffer
2010 – today
- 2013
[j14]Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, Niels Landwehr: Active evaluation of ranking functions based on graded relevance. Machine Learning 92(1): 41-64 (2013)- [c63]Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, Niels Landwehr: Active Evaluation of Ranking Functions Based on Graded Relevance (Extended Abstract). IJCAI 2013
- 2012
- [c62]
- [c61]Paul Prasse, Christoph Sawade, Niels Landwehr, Tobias Scheffer: Learning to Identify Regular Expressions that Describe Email Campaigns. ICML 2012
- [c60]Christoph Sawade, Niels Landwehr, Tobias Scheffer: Active Comparison of Prediction Models. NIPS 2012: 1763-1771
- [c59]Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, Niels Landwehr: Active Evaluation of Ranking Functions Based on Graded Relevance. ECML/PKDD (2) 2012: 676-691
- [i2]Paul Prasse, Christoph Sawade, Niels Landwehr, Tobias Scheffer: Learning to Identify Regular Expressions that Describe Email Campaigns. CoRR abs/1206.4637 (2012)
- [i1]Peter Haider, Tobias Scheffer: Finding Botnets Using Minimal Graph Clusterings. CoRR abs/1206.4675 (2012)
- 2011
- [c58]Michael Brückner, Tobias Scheffer: Stackelberg games for adversarial prediction problems. KDD 2011: 547-555
- [e5]Lise Getoor, Tobias Scheffer (Eds.): Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011. Omnipress 2011
- 2010
- [c57]Christoph Sawade, Niels Landwehr, Steffen Bickel, Tobias Scheffer: Active Risk Estimation. ICML 2010: 951-958
- [c56]Uwe Dick, Peter Haider, Thomas Vanck, Michael Brückner, Tobias Scheffer: Throttling Poisson Processes. NIPS 2010: 505-513
- [c55]Christoph Sawade, Niels Landwehr, Tobias Scheffer: Active Estimation of F-Measures. NIPS 2010: 2083-2091
2000 – 2009
- 2009
- [j13]Szymon Jaroszewicz, Tobias Scheffer, Dan A. Simovici: Scalable pattern mining with Bayesian networks as background knowledge. Data Min. Knowl. Discov. 18(1): 56-100 (2009)
- [j12]Steffen Bickel, Michael Brückner, Tobias Scheffer: Discriminative Learning Under Covariate Shift. Journal of Machine Learning Research 10: 2137-2155 (2009)
- [c54]
- [c53]
- [c52]Laura Dietz, Valentin Dallmeier, Andreas Zeller, Tobias Scheffer: Localizing Bugs in Program Executions with Graphical Models. NIPS 2009: 468-476
- [r1]Tobias Scheffer: Semi-Supervised Learning. Encyclopedia of Data Warehousing and Mining 2009: 1787-1793
- 2008
- [j11]Szymon Jaroszewicz, Lenka Ivantysynova, Tobias Scheffer: Schema matching on streams with accuracy guarantees. Intell. Data Anal. 12(3): 253-270 (2008)
- [c51]Steffen Bickel, Jasmina Bogojeska, Thomas Lengauer, Tobias Scheffer: Multi-task learning for HIV therapy screening. ICML 2008: 56-63
- [c50]Uwe Dick, Peter Haider, Tobias Scheffer: Learning from incomplete data with infinite imputations. ICML 2008: 232-239
- [c49]Steffen Bickel, Christoph Sawade, Tobias Scheffer: Transfer Learning by Distribution Matching for Targeted Advertising. NIPS 2008: 145-152
- [c48]Thoralf Klein, Ulf Brefeld, Tobias Scheffer: Exact and Approximate Inference for Annotating Graphs with Structural SVMs. ECML/PKDD (1) 2008: 611-623
- 2007
- [c47]Steffen Bickel, Michael Brückner, Tobias Scheffer: Discriminative learning for differing training and test distributions. ICML 2007: 81-88
- [c46]Laura Dietz, Steffen Bickel, Tobias Scheffer: Unsupervised prediction of citation influences. ICML 2007: 233-240
- [c45]Peter Haider, Ulf Brefeld, Tobias Scheffer: Supervised clustering of streaming data for email batch detection. ICML 2007: 345-352
- [c44]Alexander Zien, Ulf Brefeld, Tobias Scheffer: Transductive support vector machines for structured variables. ICML 2007: 1183-1190
- [c43]David S. Vogel, Ognian Asparouhov, Tobias Scheffer: Scalable look-ahead linear regression trees. KDD 2007: 757-764
- [c42]Ulf Brefeld, Thoralf Klein, Tobias Scheffer: Support Vector Machines for Collective Inference. MLG 2007
- 2006
- [c41]Ulf Brefeld, Thomas Gärtner, Tobias Scheffer, Stefan Wrobel: Efficient co-regularised least squares regression. ICML 2006: 137-144
- [c40]Ulf Brefeld, Tobias Scheffer: Semi-supervised learning for structured output variables. ICML 2006: 145-152
- [c39]Steffen Bickel, Tobias Scheffer: Dirichlet-Enhanced Spam Filtering based on Biased Samples. NIPS 2006: 161-168
- [c38]Michael Brückner, Peter Haider, Tobias Scheffer: Highly Scalable Discriminative Spam Filtering. TREC 2006
- [e4]Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou (Eds.): Machine Learning: ECML 2006, 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006, Proceedings. Lecture Notes in Computer Science 4212, Springer 2006, ISBN 3-540-45375-X
- [e3]Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou (Eds.): Knowledge Discovery in Databases: PKDD 2006, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Berlin, Germany, September 18-22, 2006, Proceedings. Lecture Notes in Computer Science 4213, Springer 2006, ISBN 3-540-45374-1
- 2005
- [j10]Jörg Hakenberg, Steffen Bickel, Conrad Plake, Ulf Brefeld, Hagen Zahn, Lukas Faulstich, Ulf Leser, Tobias Scheffer: Systematic feature evaluation for gene name recognition. BMC Bioinformatics 6(S-1) (2005)
- [j9]Tobias Scheffer: Finding association rules that trade support optimally against confidence. Intell. Data Anal. 9(4): 381-395 (2005)
- [j8]David S. Vogel, Steffen Bickel, Peter Haider, Rolf Schimpfky, Peter Siemen, Steve Bridges, Tobias Scheffer: Classifying search engine queries using the web as background knowledge. SIGKDD Explorations 7(2): 117-122 (2005)
- [c37]Tobias Scheffer: Multi-View Learning and Link Farm Discovery. Probabilistic, Logical and Relational Learning 2005
- [c36]
- [c35]Ulf Brefeld, Christoph Büscher, Tobias Scheffer: Multi-view Discriminative Sequential Learning. ECML 2005: 60-71
- [c34]Isabel Drost, Tobias Scheffer: Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam. ECML 2005: 96-107
- [c33]
- [c32]Isabel Drost, Steffen Bickel, Tobias Scheffer: Discovering Communities in Linked Data by Multi-view Clustering. GfKl 2005: 342-349
- [c31]Szymon Jaroszewicz, Tobias Scheffer: Fast discovery of unexpected patterns in data, relative to a Bayesian network. KDD 2005: 118-127
- [c30]Ulf Brefeld, Christoph Büscher, Tobias Scheffer: Multi-View Hidden Markov Perceptrons. LWA 2005: 134-138
- [c29]Steffen Bickel, Peter Haider, Tobias Scheffer: Predicting Sentences using N-Gram Language Models. HLT/EMNLP 2005
- [e2]Achim G. Hoffmann, Hiroshi Motoda, Tobias Scheffer (Eds.): Discovery Science, 8th International Conference, DS 2005, Singapore, October 8-11, 2005, Proceedings. Lecture Notes in Computer Science 3735, Springer 2005, ISBN 3-540-29230-6
- 2004
- [j7]Tobias Scheffer: Email answering assistance by semi-supervised text classification. Intell. Data Anal. 8(5): 481-493 (2004)
- [j6]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)
- [c28]Steffen Bickel, Tobias Scheffer: Learning from Message Pairs for Automatic Email Answering. ECML 2004: 87-98
- [c27]
- [c26]
- [c25]
- [c24]
- [c23]Isabel Drost, Tobias Scheffer: Efficiency and Stability of Clustering Algorithms for Linked Data. LWA 2004: 146
- [c22]
- [e1]Andreas Abecker, Steffen Bickel, Ulf Brefeld, Isabel Drost, Nicola Henze, Olaf Herden, Mirjam Minor, Tobias Scheffer, Ljiljana Stojanovic, Stephan Weibelzahl (Eds.): LWA 2004: Lernen - Wissensentdeckung - Adaptivität, Berlin, 4. - 6. Oktober 2004, Workshopwoche der GI-Fachgruppen/Arbeitskreise (1) Fachgruppe Adaptivität und Benutzermodellierung in Interaktiven Softwaresystemen (ABIS 2004), (2) Arbeitskreis Knowledge Discovery (AKKD 2004), (3) Fachgruppe Maschinelles Lernen (FGML 2004), (4) Fachgruppe Wissens- und Erfahrungsmanagement (FGWM 2004). Humbold-Universität Berlin 2004
- 2003
- [c21]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
- [c20]
- [c19]Mark-A. Krogel, Tobias Scheffer: Effectiveness of information extraction, multi-relational, and multi-view learning for prediction gene deletion experiments. BIOKDD 2003: 10-16
- [c18]Michael Kockelkorn, Andreas Lüneburg, Tobias Scheffer: Using Transduction and Multi-view Learning to Answer Emails. PKDD 2003: 266-277
- 2002
- [j5]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)
- [j4]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)
- [j3]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)
- [c17]Tobias Scheffer, Stefan Wrobel: A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases. PKDD 2002: 397-409
- 2001
- [c16]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
- [c15]Tobias Scheffer, Christian Decomain, Stefan Wrobel: Mining the Web with Active Hidden Markov Models. ICDM 2001: 645-646
- [c14]Tobias Scheffer, Stefan Wrobel: Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems. ICML 2001: 481-488
- [c13]Tobias Scheffer, Christian Decomain, Stefan Wrobel: Active Hidden Markov Models for Information Extraction. IDA 2001: 309-318
- [c12]Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence. PKDD 2001: 424-435
- 2000
- [c11]Tobias Scheffer: Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees. ALT 2000: 194-208
- [c10]
- [c9]Tobias Scheffer: Predicting the Generalization Performance of Cross Validatory Model Selection Criteria. ICML 2000: 831-838
- [c8]Tobias Scheffer, Stefan Wrobel: A sequential sampling algorithm for a general class of utility criteria. KDD 2000: 330-334
1990 – 1999
- 1999
- [b1]Tobias Scheffer: Error estimation and model selection. DISKI 225, Infix 1999, ISBN 978-3-89601-225-8, pp. I-XI, 1-126
- [j2]
- [j1]
- [c7]Andrew R. Mitchell, Tobias Scheffer, Arun Sharma, Frank Stephan: The VC-Dimension of Subclasses of Pattern. ALT 1999: 93-105
- [c6]
- 1998
- [c5]Tobias Scheffer, Thorsten Joachims: Estimating the Expected Error of Empirical Minimizers for Model Selection. AAAI/IAAI 1998: 1200
- 1997
- [c4]Tobias Scheffer, Russell Greiner, Christian Darken: Why Experimentation can be better than "Perfect Guidance". ICML 1997: 331-339
- [c3]
- 1996
- [c2]Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki: Efficient Theta-Subsumption Based on Graph Algorithms. Inductive Logic Programming Workshop 1996: 212-228
- 1995
- [c1]Tobias Scheffer: A Generic Algorithm for Learning Rules with Hierarchical Exceptions. SBIA 1995: 181-190
Coauthor Index
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last updated on 2013-10-02 11:01 CEST by the dblp team