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» On robust online scheduling algorithms
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TIP
2008
185views more  TIP 2008»
13 years 7 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
12 years 11 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»
11 years 10 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
14 years 8 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
14 years 10 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