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» Uncertainty in Soft Constraint Problems
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JMLR
2006
125views more  JMLR 2006»
13 years 7 months ago
Efficient Learning of Label Ranking by Soft Projections onto Polyhedra
We discuss the problem of learning to rank labels from a real valued feedback associated with each label. We cast the feedback as a preferences graph where the nodes of the graph ...
Shai Shalev-Shwartz, Yoram Singer
COR
2010
123views more  COR 2010»
13 years 7 months ago
Decomposition, reformulation, and diving in university course timetabling
In many real-life optimisation problems, there are multiple interacting components in a solution. For example, different components might specify assignments to different kinds of...
Edmund K. Burke, Jakub Marecek, Andrew J. Parkes, ...
NIPS
2007
13 years 9 months ago
Boosting Algorithms for Maximizing the Soft Margin
We present a novel boosting algorithm, called SoftBoost, designed for sets of binary labeled examples that are not necessarily separable by convex combinations of base hypotheses....
Manfred K. Warmuth, Karen A. Glocer, Gunnar Rä...
CONSTRAINTS
2010
126views more  CONSTRAINTS 2010»
13 years 7 months ago
Lexicographically-ordered constraint satisfaction problems
Abstract. We describe a simple CSP formalism for handling multi-attribute preference problems with hard constraints, one that combines hard constraints and preferences so the two a...
Eugene C. Freuder, Robert Heffernan, Richard J. Wa...
ICDE
2009
IEEE
170views Database» more  ICDE 2009»
14 years 2 months ago
On High Dimensional Projected Clustering of Uncertain Data Streams
— In this paper, we will study the problem of projected clustering of uncertain data streams. The use of uncertainty is especially important in the high dimensional scenario, bec...
Charu C. Aggarwal