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Tobias Scheffer: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Tobias Scheffer, Thorsten Joachims
    Estimating the Expected Error of Empirical Minimizers for Model Selection. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 1998, pp:1200- [Conf]
  2. Andrew R. Mitchell, Tobias Scheffer, Arun Sharma, Frank Stephan
    The VC-Dimension of Subclasses of Pattern. [Citation Graph (0, 0)][DBLP]
    ATL, 1999, pp:93-105 [Conf]
  3. Tobias Scheffer
    Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees. [Citation Graph (0, 0)][DBLP]
    ALT, 2000, pp:194-208 [Conf]
  4. Tobias Scheffer
    Multi-View Learning and Link Farm Discovery. [Citation Graph (0, 0)][DBLP]
    Probabilistic, Logical and Relational Learning, 2005, pp:- [Conf]
  5. Hans Gründel, Tino Naphtali, Christian Wiech, Jan-Marian Gluba, Maiken Rohdenburg, Tobias Scheffer
    Clipping and Analyzing News Using Machine Learning Techniques. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2001, pp:87-99 [Conf]
  6. Ulf Brefeld, Christoph Büscher, Tobias Scheffer
    Multi-view Discriminative Sequential Learning. [Citation Graph (0, 0)][DBLP]
    ECML, 2005, pp:60-71 [Conf]
  7. Steffen Bickel, Peter Haider, Tobias Scheffer
    Learning to Complete Sentences. [Citation Graph (0, 0)][DBLP]
    ECML, 2005, pp:497-504 [Conf]
  8. Steffen Bickel, Tobias Scheffer
    Learning from Message Pairs for Automatic Email Answering. [Citation Graph (0, 0)][DBLP]
    ECML, 2004, pp:87-98 [Conf]
  9. Steffen Bickel, Tobias Scheffer
    Estimation of Mixture Models Using Co-EM. [Citation Graph (0, 0)][DBLP]
    ECML, 2005, pp:35-46 [Conf]
  10. Isabel Drost, Tobias Scheffer
    Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam. [Citation Graph (0, 0)][DBLP]
    ECML, 2005, pp:96-107 [Conf]
  11. Tobias Scheffer
    Nonparametric Regularization of Decision Trees. [Citation Graph (0, 0)][DBLP]
    ECML, 2000, pp:344-356 [Conf]
  12. Steffen Bickel, Tobias Scheffer
    Multi-View Clustering. [Citation Graph (0, 0)][DBLP]
    ICDM, 2004, pp:19-26 [Conf]
  13. Mark-A. Krogel, Tobias Scheffer
    Effectiveness of Information Extraction, Multi-Relational, and Semi-Supervised Learning for Predicting Functional Properties of Genes. [Citation Graph (0, 0)][DBLP]
    ICDM, 2003, pp:569-572 [Conf]
  14. Tobias Scheffer, Christian Decomain, Stefan Wrobel
    Mining the Web with Active Hidden Markov Models. [Citation Graph (0, 0)][DBLP]
    ICDM, 2001, pp:645-646 [Conf]
  15. Ulf Brefeld, Thomas Gärtner, Tobias Scheffer, Stefan Wrobel
    Efficient co-regularised least squares regression. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:137-144 [Conf]
  16. Ulf Brefeld, Tobias Scheffer
    Co-EM support vector learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  17. Ulf Brefeld, Tobias Scheffer
    Semi-supervised learning for structured output variables. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:145-152 [Conf]
  18. Tobias Scheffer
    Predicting the Generalization Performance of Cross Validatory Model Selection Criteria. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:831-838 [Conf]
  19. Tobias Scheffer, Russell Greiner, Christian Darken
    Why Experimentation can be better than "Perfect Guidance". [Citation Graph (0, 0)][DBLP]
    ICML, 1997, pp:331-339 [Conf]
  20. Tobias Scheffer, Thorsten Joachims
    Expected Error Analysis for Model Selection. [Citation Graph (0, 0)][DBLP]
    ICML, 1999, pp:361-370 [Conf]
  21. Tobias Scheffer, Stefan Wrobel
    Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems. [Citation Graph (0, 0)][DBLP]
    ICML, 2001, pp:481-488 [Conf]
  22. Michael Kockelkorn, Andreas Lüneburg, Tobias Scheffer
    Learning to Answer Emails. [Citation Graph (0, 0)][DBLP]
    IDA, 2003, pp:25-35 [Conf]
  23. Tobias Scheffer, Christian Decomain, Stefan Wrobel
    Active Hidden Markov Models for Information Extraction. [Citation Graph (0, 0)][DBLP]
    IDA, 2001, pp:309-318 [Conf]
  24. Tobias Scheffer, Ralf Herbrich
    Unbiased Assesment of Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    IJCAI (2), 1997, pp:798-803 [Conf]
  25. Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki
    Efficient Theta-Subsumption Based on Graph Algorithms. [Citation Graph (0, 0)][DBLP]
    Inductive Logic Programming Workshop, 1996, pp:212-228 [Conf]
  26. Szymon Jaroszewicz, Tobias Scheffer
    Fast discovery of unexpected patterns in data, relative to a Bayesian network. [Citation Graph (0, 0)][DBLP]
    KDD, 2005, pp:118-127 [Conf]
  27. Mark-A. Krogel, Tobias Scheffer
    Effectiveness of information extraction, multi-relational, and multi-view learning for prediction gene deletion experiments. [Citation Graph (0, 0)][DBLP]
    BIOKDD, 2003, pp:10-16 [Conf]
  28. Tobias Scheffer, Stefan Wrobel
    A sequential sampling algorithm for a general class of utility criteria. [Citation Graph (0, 0)][DBLP]
    KDD, 2000, pp:330-334 [Conf]
  29. Tobias Scheffer
    Workshop der GI-Fachgruppe "Maschinelles Lernen" (FGML). [Citation Graph (0, 0)][DBLP]
    LWA, 2004, pp:110- [Conf]
  30. Ulf Brefeld, Steffen Bickel, Tobias Scheffer
    Multi-View Lernen. [Citation Graph (0, 0)][DBLP]
    LWA, 2004, pp:131- [Conf]
  31. Ulf Brefeld, Christoph Büscher, Tobias Scheffer
    Multi-View Hidden Markov Perceptrons. [Citation Graph (0, 0)][DBLP]
    LWA, 2005, pp:134-138 [Conf]
  32. Isabel Drost, Tobias Scheffer
    Efficiency and Stability of Clustering Algorithms for Linked Data. [Citation Graph (0, 0)][DBLP]
    LWA, 2004, pp:146- [Conf]
  33. Steffen Bickel, Peter Haider, Tobias Scheffer
    Predicting Sentences using N-Gram Language Models. [Citation Graph (0, 0)][DBLP]
    HLT/EMNLP, 2005, pp:- [Conf]
  34. Michael Kockelkorn, Andreas Lüneburg, Tobias Scheffer
    Using Transduction and Multi-view Learning to Answer Emails. [Citation Graph (0, 0)][DBLP]
    PKDD, 2003, pp:266-277 [Conf]
  35. Tobias Scheffer
    Finding Association Rules That Trade Support Optimally against Confidence. [Citation Graph (0, 0)][DBLP]
    PKDD, 2001, pp:424-435 [Conf]
  36. Tobias Scheffer, Stefan Wrobel
    A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases. [Citation Graph (0, 0)][DBLP]
    PKDD, 2002, pp:397-409 [Conf]
  37. Tobias Scheffer
    A Generic Algorithm for Learning Rules with Hierarchical Exceptions. [Citation Graph (0, 0)][DBLP]
    SBIA, 1995, pp:181-190 [Conf]
  38. Korinna Grabski, Tobias Scheffer
    Sentence completion. [Citation Graph (0, 0)][DBLP]
    SIGIR, 2004, pp:433-439 [Conf]
  39. Tobias Scheffer
    Email answering assistance by semi-supervised text classification. [Citation Graph (0, 0)][DBLP]
    Intell. Data Anal., 2004, v:8, n:5, pp:481-493 [Journal]
  40. Tobias Scheffer
    Finding association rules that trade support optimally against confidence. [Citation Graph (0, 0)][DBLP]
    Intell. Data Anal., 2005, v:9, n:4, pp:381-395 [Journal]
  41. Tobias Scheffer, Stefan Wrobel
    Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2002, v:3, n:, pp:833-862 [Journal]
  42. Tobias Scheffer
    International Conference on Machine Learning (ICML-99). [Citation Graph (0, 0)][DBLP]
    KI, 1999, v:13, n:4, pp:68- [Journal]
  43. Tobias Scheffer
    Error Estimation and Model Selection. [Citation Graph (0, 0)][DBLP]
    KI, 1999, v:13, n:3, pp:46-48 [Journal]
  44. Tobias Scheffer, Stefan Wrobel, Borislav Popov, Damyan Ognianov, Christian Decomain, Susanne Hoche
    Lerning Hidden Markov Models for Information Extraction Actively from Partially Labeled Text. [Citation Graph (0, 0)][DBLP]
    KI, 2002, v:16, n:2, pp:17-22 [Journal]
  45. Mark-A. Krogel, Tobias Scheffer
    Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:57, n:1-2, pp:61-81 [Journal]
  46. Mark-A. Krogel, Marcus Denecke, Marco Landwehr, Tobias Scheffer
    Combining Data and Text Mining Techniques for Yeast Gene Regulation Prediction: A Case Study. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2002, v:4, n:2, pp:104-105 [Journal]
  47. 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. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2005, v:7, n:2, pp:117-122 [Journal]
  48. Peter Haider, Ulf Brefeld, Tobias Scheffer
    Supervised clustering of streaming data for email batch detection. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:345-352 [Conf]
  49. Laura Dietz, Steffen Bickel, Tobias Scheffer
    Unsupervised prediction of citation influences. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:233-240 [Conf]
  50. Alexander Zien, Ulf Brefeld, Tobias Scheffer
    Transductive support vector machines for structured variables. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:1183-1190 [Conf]
  51. Steffen Bickel, Michael Brückner, Tobias Scheffer
    Discriminative learning for differing training and test distributions. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:81-88 [Conf]
  52. David S. Vogel, Ognian Asparouhov, Tobias Scheffer
    Scalable look-ahead linear regression trees. [Citation Graph (0, 0)][DBLP]
    KDD, 2007, pp:757-764 [Conf]
  53. Steffen Bickel, Tobias Scheffer
    Dirichlet-Enhanced Spam Filtering based on Biased Samples. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:161-168 [Conf]

  54. Learning from incomplete data with infinite imputations. [Citation Graph (, )][DBLP]


  55. Multi-task learning for HIV therapy screening. [Citation Graph (, )][DBLP]


  56. Bayesian clustering for email campaign detection. [Citation Graph (, )][DBLP]


  57. Active Risk Estimation. [Citation Graph (, )][DBLP]


  58. Transfer Learning by Distribution Matching for Targeted Advertising. [Citation Graph (, )][DBLP]


  59. Exact and Approximate Inference for Annotating Graphs with Structural SVMs. [Citation Graph (, )][DBLP]


  60. Highly Scalable Discriminative Spam Filtering. [Citation Graph (, )][DBLP]


  61. Support Vector Machines for Collective Inference. [Citation Graph (, )][DBLP]


  62. Discovering Communities in Linked Data by Multi-view Clustering. [Citation Graph (, )][DBLP]


  63. Systematic feature evaluation for gene name recognition. [Citation Graph (, )][DBLP]


  64. Scalable pattern mining with Bayesian networks as background knowledge. [Citation Graph (, )][DBLP]


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