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» On the sample mean of graphs
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TIP
2010
155views more  TIP 2010»
14 years 8 months ago
Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval
—In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels ...
Xiaofei He
FOCS
2007
IEEE
15 years 4 months ago
Adaptive Simulated Annealing: A Near-optimal Connection between Sampling and Counting
We present a near-optimal reduction from approximately counting the cardinality of a discrete set to approximately sampling elements of the set. An important application of our wo...
Daniel Stefankovic, Santosh Vempala, Eric Vigoda
BMCBI
2008
107views more  BMCBI 2008»
14 years 10 months ago
A mixture model approach to sample size estimation in two-sample comparative microarray experiments
Background: Choosing the appropriate sample size is an important step in the design of a microarray experiment, and recently methods have been proposed that estimate sample sizes ...
Tommy S. Jørstad, Herman Midelfart, Atle M....
WSC
2007
15 years 12 days ago
Single-stage multiple-comparison procedure for quantiles and other parameters
We present a single-stage multiple-comparison procedure for comparing parameters of independent systems, where the parameters are not necessarily means or steady-state means. We a...
Marvin K. Nakayama
83
Voted
SDM
2009
SIAM
123views Data Mining» more  SDM 2009»
15 years 7 months ago
Randomization Techniques for Graphs.
Mining graph data is an active research area. Several data mining methods and algorithms have been proposed to identify structures from graphs; still, the evaluation of those resu...
Gemma C. Garriga, Kai Puolamäki, Sami Hanhij&...