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GECCO
2008
Springer
137views Optimization» more  GECCO 2008»
13 years 8 months ago
Informative sampling for large unbalanced data sets
Selective sampling is a form of active learning which can reduce the cost of training by only drawing informative data points into the training set. This selected training set is ...
Zhenyu Lu, Anand I. Rughani, Bruce I. Tranmer, Jos...
CIKM
2010
Springer
13 years 6 months ago
Novel local features with hybrid sampling technique for image retrieval
In image retrieval, most existing approaches that incorporate local features produce high dimensional vectors, which lead to a high computational and data storage cost. Moreover, ...
Leszek Kaliciak, Dawei Song, Nirmalie Wiratunga, J...
19
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ISPASS
2003
IEEE
14 years 1 months ago
Memory reference reuse latency: Accelerated warmup for sampled microarchitecture simulation
Abstract— This paper proposes to speedup sampled microprocessor simulations by reducing warmup times without sacrificing simulation accuracy. It exploiting the observation that ...
John W. Haskins Jr., Kevin Skadron
BMCBI
2006
98views more  BMCBI 2006»
13 years 7 months ago
Scanning sequences after Gibbs sampling to find multiple occurrences of functional elements
Background: Many DNA regulatory elements occur as multiple instances within a target promoter. Gibbs sampling programs for finding DNA regulatory elements de novo can be prohibiti...
Kannan Tharakaraman, Leonardo Mariño-Ram&ia...
CGF
2007
158views more  CGF 2007»
13 years 7 months ago
Manipulating, Deforming and Animating Sampled Object Representations
A sampled object representation (SOR) defines a graphical model using data obtained from a sampling process, which takes a collection of samples at discrete positions in space in...
Min Chen, Carlos D. Correa, Shoukat Islam, Mark W....