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ORCIDUlrich Rückert 0002
Person information
- affiliation: University of California, Berkeley, International Computer Science Institute
- affiliation: TU Munich, Department of Computer Science
Other persons with the same name
- Ulrich Rückert 0001 — University of Bielefeld, Faculty of Technology, Germany (and 1 more)
- Ulrich Rückert 0003 — Google AI
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2010 – 2019
- 2013
[j5]Tobias Girschick, Ulrich Rückert, Stefan Kramer:
Adapted Transfer of Distance Measures for Quantitative Structure-Activity Relationships and Data-Driven Selection of Source Datasets. Comput. J. 56(3): 274-288 (2013)- 2012
- [j4]Richard Röttger
, Ulrich Rückert, Jan Taubert, Jan Baumbach:
How Little Do We Actually Know? On the Size of Gene Regulatory Networks. IEEE ACM Trans. Comput. Biol. Bioinform. 9(5): 1293-1300 (2012) - 2011
- [c18]Ulrich Rückert, Marius Kloft:
Transfer Learning with Adaptive Regularizers. ECML/PKDD (3) 2011: 65-80 - 2010
- [c17]Ulrich Rückert, Tobias Girschick, Fabian Buchwald, Stefan Kramer:
Adapted Transfer of Distance Measures for Quantitative Structure-Activity Relationships. Discovery Science 2010: 341-355 - [c16]Marius Kloft, Ulrich Rückert, Peter L. Bartlett:
A Unifying View of Multiple Kernel Learning. ECML/PKDD (2) 2010: 66-81 - [c15]Albrecht Zimmermann, Björn Bringmann, Ulrich Rückert:
Fast, Effective Molecular Feature Mining by Local Optimization. ECML/PKDD (3) 2010: 563-578 - [i1]Marius Kloft, Ulrich Rückert, Peter L. Bartlett:
A Unifying View of Multiple Kernel Learning. CoRR abs/1005.0437 (2010)
2000 – 2009
- 2009
- [j3]Anton Dries, Ulrich Rückert:
Adaptive concept drift detection. Stat. Anal. Data Min. 2(5-6): 311-327 (2009) - [c14]Ulrich Rückert:
Capacity Control for Partially Ordered Feature Sets. ECML/PKDD (2) 2009: 318-333 - [c13]Anton Dries, Ulrich Rückert:
Adaptive Concept Drift Detection. SDM 2009: 235-246 - 2008
- [b1]Ulrich Rückert:
A statistical approach to rule learning. Technical University Munich, Germany, 2008, pp. 1-202 - [j2]Ulrich Rückert, Luc De Raedt:
An experimental evaluation of simplicity in rule learning. Artif. Intell. 172(1): 19-28 (2008) - [j1]Ulrich Rückert, Stefan Kramer:
Margin-based first-order rule learning. Mach. Learn. 70(2-3): 189-206 (2008) - [c12]Ulrich Rückert, Stefan Kramer:
Kernel-Based Inductive Transfer. ECML/PKDD (2) 2008: 220-233 - 2007
- [c11]Ulrich Rückert, Stefan Kramer:
Optimizing Feature Sets for Structured Data. ECML 2007: 716-723 - 2006
- [c10]Ulrich Rückert, Stefan Kramer:
A statistical approach to rule learning. ICML 2006: 785-792 - [c9]Ulrich Rückert, Stefan Kramer:
Margin-Based First-Order Rule Learning. ILP 2006: 46-48 - [c8]Lothar Richter, Ulrich Rückert, Stefan Kramer:
Learning a Predictive Model for Growth Inhibition from the NCI DTP Human Tumor Cell Line Screening Data: Does Gene Expression Make a Difference? Pacific Symposium on Biocomputing 2006: 596-607 - 2005
- [c7]Elisabeth Georgii, Lothar Richter, Ulrich Rückert, Stefan Kramer:
Analyzing microarray data using quantitative association rules. ECCB/JBI 2005: 129 - 2004
- [c6]Ulrich Rückert, Lothar Richter, Stefan Kramer:
Quantitative Association Rules Based on Half-Spaces: An Optimization Approach. ICDM 2004: 507-510 - [c5]Ulrich Rückert, Stefan Kramer:
Towards tight bounds for rule learning. ICML 2004 - [c4]Ulrich Rückert, Stefan Kramer:
Frequent free tree discovery in graph data. SAC 2004: 564-570 - 2003
- [c3]Ulrich Rückert, Stefan Kramer:
Stochastic Local Search in k-Term DNF Learning. ICML 2003: 648-655 - [c2]Ulrich Rückert, Stefan Kramer:
Generalized Version Space Trees. KDID 2003: 119-129 - 2002
- [c1]Ulrich Rückert, Stefan Kramer, Luc De Raedt:
Phase Transitions and Stochastic Local Search in k-Term DNF Learning. ECML 2002: 405-417
Coauthor Index
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