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» Model selection by sequentially normalized least squares
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WSC
2000
13 years 9 months ago
Ranking and selection for steady-state simulation
We present and evaluate two ranking-and-selection procedures for use in steady-state simulation experiments when the goal is to find which among a finite number of alternative sys...
David Goldsman, William S. Marshall, Seong-Hee Kim...
ICRA
2010
IEEE
148views Robotics» more  ICRA 2010»
13 years 6 months ago
Body schema acquisition through active learning
— We present an active learning algorithm for the problem of body schema learning, i.e. estimating a kinematic model of a serial robot. The learning process is done online using ...
Ruben Martinez-Cantin, Manuel Lopes, Luis Montesan...
BMCBI
2010
165views more  BMCBI 2010»
13 years 7 months ago
Multivariate meta-analysis of proteomics data from human prostate and colon tumours
Background: There is a vast need to find clinically applicable protein biomarkers as support in cancer diagnosis and tumour classification. In proteomics research, a number of met...
Lina Hultin Rosenberg, Bo Franzén, Gert Aue...
EOR
2006
113views more  EOR 2006»
13 years 7 months ago
Validation of regression metamodels in simulation: Bootstrap approach
Simulation experiments are often analyzed through a linear regression model of their input/output data. Such an analysis yields a metamodel or response surface for the underlying ...
Jack P. C. Kleijnen, David Deflandre
ICML
2007
IEEE
14 years 8 months ago
Kernelizing PLS, degrees of freedom, and efficient model selection
Kernelizing partial least squares (PLS), an algorithm which has been particularly popular in chemometrics, leads to kernel PLS which has several interesting properties, including ...
Mikio L. Braun, Nicole Krämer