| 2009 |
|---|
| 65 | EE | 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) |
| 2008 |
|---|
| 64 | EE | Thoralf Klein,
Ulf Brefeld,
Tobias Scheffer:
Exact and Approximate Inference for Annotating Graphs with Structural SVMs.
ECML/PKDD (1) 2008: 611-623 |
| 63 | EE | Uwe Dick,
Peter Haider,
Tobias Scheffer:
Learning from incomplete data with infinite imputations.
ICML 2008: 232-239 |
| 62 | EE | Steffen Bickel,
Jasmina Bogojeska,
Thomas Lengauer,
Tobias Scheffer:
Multi-task learning for HIV therapy screening.
ICML 2008: 56-63 |
| 61 | EE | Steffen Bickel,
Christoph Sawade,
Tobias Scheffer:
Transfer Learning by Distribution Matching for Targeted Advertising.
NIPS 2008: 145-152 |
| 60 | EE | Szymon Jaroszewicz,
Lenka Ivantysynova,
Tobias Scheffer:
Schema matching on streams with accuracy guarantees.
Intell. Data Anal. 12(3): 253-270 (2008) |
| 2007 |
|---|
| 59 | EE | Alexander Zien,
Ulf Brefeld,
Tobias Scheffer:
Transductive support vector machines for structured variables.
ICML 2007: 1183-1190 |
| 58 | EE | Laura Dietz,
Steffen Bickel,
Tobias Scheffer:
Unsupervised prediction of citation influences.
ICML 2007: 233-240 |
| 57 | EE | Peter Haider,
Ulf Brefeld,
Tobias Scheffer:
Supervised clustering of streaming data for email batch detection.
ICML 2007: 345-352 |
| 56 | EE | Steffen Bickel,
Michael Brückner,
Tobias Scheffer:
Discriminative learning for differing training and test distributions.
ICML 2007: 81-88 |
| 55 | EE | David S. Vogel,
Ognian Asparouhov,
Tobias Scheffer:
Scalable look-ahead linear regression trees.
KDD 2007: 757-764 |
| 54 | EE | Ulf Brefeld,
Thoralf Klein,
Tobias Scheffer:
Support Vector Machines for Collective Inference.
MLG 2007 |
| 2006 |
|---|
| 53 | | Johannes Fürnkranz,
Tobias Scheffer,
Myra Spiliopoulou:
Machine Learning: ECML 2006, 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006, Proceedings
Springer 2006 |
| 52 | | Johannes Fürnkranz,
Tobias Scheffer,
Myra Spiliopoulou:
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
Springer 2006 |
| 51 | EE | Ulf Brefeld,
Thomas Gärtner,
Tobias Scheffer,
Stefan Wrobel:
Efficient co-regularised least squares regression.
ICML 2006: 137-144 |
| 50 | EE | Ulf Brefeld,
Tobias Scheffer:
Semi-supervised learning for structured output variables.
ICML 2006: 145-152 |
| 49 | EE | Steffen Bickel,
Tobias Scheffer:
Dirichlet-Enhanced Spam Filtering based on Biased Samples.
NIPS 2006: 161-168 |
| 48 | EE | Michael Brückner,
Peter Haider,
Tobias Scheffer:
Highly Scalable Discriminative Spam Filtering.
TREC 2006 |
| 2005 |
|---|
| 47 | | Achim G. Hoffmann,
Hiroshi Motoda,
Tobias Scheffer:
Discovery Science, 8th International Conference, DS 2005, Singapore, October 8-11, 2005, Proceedings
Springer 2005 |
| 46 | EE | Steffen Bickel,
Tobias Scheffer:
Estimation of Mixture Models Using Co-EM.
ECML 2005: 35-46 |
| 45 | EE | Steffen Bickel,
Peter Haider,
Tobias Scheffer:
Learning to Complete Sentences.
ECML 2005: 497-504 |
| 44 | EE | Ulf Brefeld,
Christoph Büscher,
Tobias Scheffer:
Multi-view Discriminative Sequential Learning.
ECML 2005: 60-71 |
| 43 | EE | Isabel Drost,
Tobias Scheffer:
Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam.
ECML 2005: 96-107 |
| 42 | EE | Steffen Bickel,
Peter Haider,
Tobias Scheffer:
Predicting Sentences using N-Gram Language Models.
HLT/EMNLP 2005 |
| 41 | EE | Szymon Jaroszewicz,
Tobias Scheffer:
Fast discovery of unexpected patterns in data, relative to a Bayesian network.
KDD 2005: 118-127 |
| 40 | | Ulf Brefeld,
Christoph Büscher,
Tobias Scheffer:
Multi-View Hidden Markov Perceptrons.
LWA 2005: 134-138 |
| 39 | EE | Tobias Scheffer:
Multi-View Learning and Link Farm Discovery.
Probabilistic, Logical and Relational Learning 2005 |
| 38 | EE | Tobias Scheffer:
Finding association rules that trade support optimally against confidence.
Intell. Data Anal. 9(4): 381-395 (2005) |
| 37 | EE | 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) |
| 2004 |
|---|
| 36 | | Andreas Abecker,
Steffen Bickel,
Ulf Brefeld,
Isabel Drost,
Nicola Henze,
Olaf Herden,
Mirjam Minor,
Tobias Scheffer,
Ljiljana Stojanovic,
Stephan Weibelzahl:
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 |
| 35 | EE | Steffen Bickel,
Tobias Scheffer:
Learning from Message Pairs for Automatic Email Answering.
ECML 2004: 87-98 |
| 34 | EE | Steffen Bickel,
Tobias Scheffer:
Multi-View Clustering.
ICDM 2004: 19-26 |
| 33 | EE | Ulf Brefeld,
Tobias Scheffer:
Co-EM support vector learning.
ICML 2004 |
| 32 | | Tobias Scheffer:
Workshop der GI-Fachgruppe "Maschinelles Lernen" (FGML).
LWA 2004: 110 |
| 31 | | Ulf Brefeld,
Steffen Bickel,
Tobias Scheffer:
Multi-View Lernen.
LWA 2004: 131 |
| 30 | | Isabel Drost,
Tobias Scheffer:
Efficiency and Stability of Clustering Algorithms for Linked Data.
LWA 2004: 146 |
| 29 | EE | Korinna Grabski,
Tobias Scheffer:
Sentence completion.
SIGIR 2004: 433-439 |
| 28 | EE | Tobias Scheffer:
Email answering assistance by semi-supervised text classification.
Intell. Data Anal. 8(5): 481-493 (2004) |
| 27 | EE | 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) |
| 2003 |
|---|
| 26 | EE | Mark-A. Krogel,
Tobias Scheffer:
Effectiveness of information extraction, multi-relational, and multi-view learning for prediction gene deletion experiments.
BIOKDD 2003: 10-16 |
| 25 | EE | 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 |
| 24 | EE | Michael Kockelkorn,
Andreas Lüneburg,
Tobias Scheffer:
Learning to Answer Emails.
IDA 2003: 25-35 |
| 23 | EE | Michael Kockelkorn,
Andreas Lüneburg,
Tobias Scheffer:
Using Transduction and Multi-view Learning to Answer Emails.
PKDD 2003: 266-277 |
| 2002 |
|---|
| 22 | EE | Tobias Scheffer,
Stefan Wrobel:
A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases.
PKDD 2002: 397-409 |
| 21 | EE | 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) |
| 20 | | 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) |
| 19 | EE | 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) |
| 2001 |
|---|
| 18 | EE | 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 |
| 17 | EE | Tobias Scheffer,
Christian Decomain,
Stefan Wrobel:
Mining the Web with Active Hidden Markov Models.
ICDM 2001: 645-646 |
| 16 | | Tobias Scheffer,
Stefan Wrobel:
Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems.
ICML 2001: 481-488 |
| 15 | EE | Tobias Scheffer,
Christian Decomain,
Stefan Wrobel:
Active Hidden Markov Models for Information Extraction.
IDA 2001: 309-318 |
| 14 | EE | Tobias Scheffer:
Finding Association Rules That Trade Support Optimally against Confidence.
PKDD 2001: 424-435 |
| 2000 |
|---|
| 13 | EE | Tobias Scheffer:
Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees.
ALT 2000: 194-208 |
| 12 | EE | Tobias Scheffer:
Nonparametric Regularization of Decision Trees.
ECML 2000: 344-356 |
| 11 | | Tobias Scheffer:
Predicting the Generalization Performance of Cross Validatory Model Selection Criteria.
ICML 2000: 831-838 |
| 10 | EE | Tobias Scheffer,
Stefan Wrobel:
A sequential sampling algorithm for a general class of utility criteria.
KDD 2000: 330-334 |
| 1999 |
|---|
| 9 | EE | Andrew R. Mitchell,
Tobias Scheffer,
Arun Sharma,
Frank Stephan:
The VC-Dimension of Subclasses of Pattern.
ATL 1999: 93-105 |
| 8 | | Tobias Scheffer,
Thorsten Joachims:
Expected Error Analysis for Model Selection.
ICML 1999: 361-370 |
| 7 | | Tobias Scheffer:
Error Estimation and Model Selection.
KI 13(3): 46-48 (1999) |
| 6 | | Tobias Scheffer:
International Conference on Machine Learning (ICML-99).
KI 13(4): 68 (1999) |
| 1998 |
|---|
| 5 | | Tobias Scheffer,
Thorsten Joachims:
Estimating the Expected Error of Empirical Minimizers for Model Selection.
AAAI/IAAI 1998: 1200 |
| 1997 |
|---|
| 4 | | Tobias Scheffer,
Russell Greiner,
Christian Darken:
Why Experimentation can be better than "Perfect Guidance".
ICML 1997: 331-339 |
| 3 | | Tobias Scheffer,
Ralf Herbrich:
Unbiased Assesment of Learning Algorithms.
IJCAI (2) 1997: 798-803 |
| 1996 |
|---|
| 2 | | Tobias Scheffer,
Ralf Herbrich,
Fritz Wysotzki:
Efficient Theta-Subsumption Based on Graph Algorithms.
Inductive Logic Programming Workshop 1996: 212-228 |
| 1995 |
|---|
| 1 | | Tobias Scheffer:
A Generic Algorithm for Learning Rules with Hierarchical Exceptions.
SBIA 1995: 181-190 |
