Ulrich Rückert

UC Berkeley

other persons with the same name:

• Ulrich Rückert - Universität Bielefeld

	2013
j4		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)
	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)
	2009
j3		Anton Dries, Ulrich Rückert: Adaptive concept drift detection. Statistical Analysis and Data Mining 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: 233-244
	2008
b1		Ulrich Rückert: A statistical approach to rule learning. Technical University Munich 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. Machine Learning 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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