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» On Risky Methods for Local Selection under Noise
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BMCBI
2010
110views more  BMCBI 2010»
13 years 10 months ago
Discovering local patterns of co - evolution: computational aspects and biological examples
Background: Co-evolution is the process in which two (or more) sets of orthologs exhibit a similar or correlative pattern of evolution. Co-evolution is a powerful way to learn abo...
Tamir Tuller, Yifat Felder, Martin Kupiec
ICIP
2003
IEEE
14 years 11 months ago
Blood vessel segmentation using moving-window robust automatic threshold selection
Two moving-window methods, using either flat or Gaussian weighted windows, for local thresholding with Robust Automatic Threshold Selection are developed. The results show that fa...
Michael H. F. Wilkinson, T. Wijbenga, G. de Vries,...
TIP
2010
161views more  TIP 2010»
13 years 4 months ago
Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content
Across the field of inverse problems in image and video processing, nearly all algorithms have various parameters which need to be set in order to yield good results. In practice, ...
Xiang Zhu, Peyman Milanfar
BMCBI
2006
114views more  BMCBI 2006»
13 years 10 months ago
Evaluation and comparison of mammalian subcellular localization prediction methods
Background: Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function o...
Josefine Sprenger, J. Lynn Fink, Rohan D. Teasdale
CIKM
2009
Springer
14 years 4 months ago
Reducing the risk of query expansion via robust constrained optimization
We introduce a new theoretical derivation, evaluation methods, and extensive empirical analysis for an automatic query expansion framework in which model estimation is cast as a r...
Kevyn Collins-Thompson