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» Learning and Inference with Constraints
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CP
2009
Springer
16 years 25 days ago
Why Cumulative Decomposition Is Not as Bad as It Sounds
Abstract. The global cumulative constraint was proposed for modelling cumulative resources in scheduling problems for finite domain (FD) propagation. Since that time a great deal ...
Andreas Schutt, Thibaut Feydy, Peter J. Stuckey, M...
ISBI
2007
IEEE
16 years 9 days ago
Dynamic Texture Based Heart Localization and Segmentation in 4-D Cardiac Images
In this paper we present a dynamic texture based motion segmentation approach to address the challenging problem of heart localization and segmentation in 4D Spatio-temporal cardi...
Junzhou Huang, Xiaolei Huang, Dimitris N. Metaxas,...
GFKL
2007
Springer
148views Data Mining» more  GFKL 2007»
16 years 5 days ago
Information Integration of Partially Labeled Data
Abstract. A central task when integrating data from different sources is to detect identical items. For example, price comparison websites have to identify offers for identical p...
Steffen Rendle, Lars Schmidt-Thieme
NIPS
2008
15 years 7 months ago
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
This work characterizes the generalization ability of algorithms whose predictions are linear in the input vector. To this end, we provide sharp bounds for Rademacher and Gaussian...
Sham M. Kakade, Karthik Sridharan, Ambuj Tewari
CDC
2008
IEEE
145views Control Systems» more  CDC 2008»
15 years 6 months ago
Necessary and sufficient conditions for success of the nuclear norm heuristic for rank minimization
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This c...
Benjamin Recht, Weiyu Xu, Babak Hassibi