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» Algorithms for Index-Assisted Selectivity Estimation
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93
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ICMCS
2006
IEEE
105views Multimedia» more  ICMCS 2006»
15 years 6 months ago
Entropy and Memory Constrained Vector Quantization with Separability Based Feature Selection
An iterative model selection algorithm is proposed. The algorithm seeks relevant features and an optimal number of codewords (or codebook size) as part of the optimization. We use...
Sangho Yoon, Robert M. Gray
101
Voted
WEBI
2004
Springer
15 years 5 months ago
Estimating Size of Search Engines in an Uncooperative Environment
The number of documents that are indexed by a search engine is referred to as the size of the search engine. The information about the size of each underlying search engine is ess...
Surendra Karnatapu, Karthik Ramachandran, Zonghuan...
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...
ICPR
2002
IEEE
15 years 5 months ago
Better Features to Track by Estimating the Tracking Convergence Region
Reliably tracking key points and textured patches from frame to frame is the basic requirement for many bottomup computer vision algorithms. The problem of selecting the features ...
Zoran Zivkovic, Ferdinand van der Heijden
110
Voted
ICIP
2002
IEEE
16 years 2 months ago
Efficient selection of image patches with high motion confidence
Motion confidence measures aim to identify how well an image patch determines image motion. These kinds of confidence measures are commonly used to select points for optical flow ...
Peter Sand, Leonard McMillan