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» TRUST-TECH based Methods for Optimization and Learning
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CVPR
2005
IEEE
16 years 2 months ago
WaldBoost - Learning for Time Constrained Sequential Detection
: In many computer vision classification problems, both the error and time characterizes the quality of a decision. We show that such problems can be formalized in the framework of...
Jan Sochman, Jiri Matas
ICCV
2011
IEEE
14 years 21 days ago
Optical Flow Estimation Using Learned Sparse Model
Optical flow estimation is a fundamental and ill-posed problem in computer vision. To recover a dense flow field, appropriate spatial constraints have to be enforced. Recent ad...
Kui Jia, Xiaogang Wang, Xiaoou Tang
TASE
2008
IEEE
15 years 18 days ago
Optimization of Joint Replacement Policies for Multipart Systems by a Rollout Framework
Maintaining an asset with life-limited parts, e.g., a jet engine or an electric generator, may be costly. Certain costs, e.g., setup cost, can be shared if some parts of the asset ...
Tao Sun, Qianchuan Zhao, Peter B. Luh, Robert N. T...
102
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TVLSI
2010
14 years 7 months ago
Discrete Buffer and Wire Sizing for Link-Based Non-Tree Clock Networks
Clock network is a vulnerable victim of variations as well as a main power consumer in many integrated circuits. Recently, link-based non-tree clock network attracts people's...
Rupak Samanta, Jiang Hu, Peng Li
ECML
2005
Springer
15 years 6 months ago
Active Learning for Probability Estimation Using Jensen-Shannon Divergence
Active selection of good training examples is an important approach to reducing data-collection costs in machine learning; however, most existing methods focus on maximizing classi...
Prem Melville, Stewart M. Yang, Maytal Saar-Tsecha...