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» On robust online scheduling algorithms
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TIP
2008
185views more  TIP 2008»
15 years 5 months ago
Active Learning Methods for Interactive Image Retrieval
Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensio...
Philippe Henri Gosselin, Matthieu Cord
CVPR
2011
IEEE
14 years 9 months ago
A Non-convex Relaxation Approach to Sparse Dictionary Learning
Dictionary learning is a challenging theme in computer vision. The basic goal is to learn a sparse representation from an overcomplete basis set. Most existing approaches employ a...
Jianping Shi, Xiang Ren, Jingdong Wang, Guang Dai,...
TKDE
2012
226views Formal Methods» more  TKDE 2012»
13 years 8 months ago
DDD: A New Ensemble Approach for Dealing with Concept Drift
—Online learning algorithms often have to operate in the presence of concept drifts. A recent study revealed that different diversity levels in an ensemble of learning machines a...
Leandro L. Minku, Xin Yao
ICALP
2009
Springer
16 years 6 months ago
Sleep with Guilt and Work Faster to Minimize Flow Plus Energy
In this paper we extend the study of flow-energy scheduling to a model that allows both sleep management and speed scaling. Our main result is a sleep management algorithm called I...
Tak Wah Lam, Lap-Kei Lee, Hing-Fung Ting, Isaac Ka...
CVPR
2005
IEEE
16 years 7 months ago
Combining Object and Feature Dynamics in Probabilistic Tracking
Objects can exhibit different dynamics at different scales, and this is often exploited by visual tracking algorithms. A local dynamic model is typically used to extract image fea...
Leonid Taycher, John W. Fisher III, Trevor Darrell