Non-linearly increasing resampling in racing algorithms

TY - ABST

T1 - Non-linearly increasing resampling in racing algorithms

AU - Heidrich-Meisner, V.

AU - Igel, Christian

N1 - Conference code: 19

PY - 2011

Y1 - 2011

N2 - Racing algorithms are iterative methods for identifying the best among several options with high probability. The quality of each option is a random variable. It is estimated by its empirical mean and concentration bounds obtained from repeated sampling. In each iteration of a standard racing algorithm each promising option is reevaluated once before being statistically compared with its competitors. We argue that Hoeffding and empirical Bernstein races benefit from generalizing the functional dependence of the racing iteration and the number of samples per option and illustrate this on an artificial benchmark problem.

AB - Racing algorithms are iterative methods for identifying the best among several options with high probability. The quality of each option is a random variable. It is estimated by its empirical mean and concentration bounds obtained from repeated sampling. In each iteration of a standard racing algorithm each promising option is reevaluated once before being statistically compared with its competitors. We argue that Hoeffding and empirical Bernstein races benefit from generalizing the functional dependence of the racing iteration and the number of samples per option and illustrate this on an artificial benchmark problem.

M3 - Conference abstract in proceedings

SN - 978-2-87419-044-5

SP - 465

EP - 470

BT - 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2011)

A2 - Verleysen, M.

T2 - 19th European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning

Y2 - 27 April 2011 through 27 April 2011

ER -