The Oracle approach tracks the quantile and associated quantile loss for a wide range of values of the algorithm parameters and, in each iteration, selects the quantile estimate with the minimal estimated quantile loss.
Apr 14, 2022
Apr 27, 2020 · The experiments show that the procedures are highly efficient and track quantiles with an error close to the theoretical optimum. The Oracle ...
This paper suggests using expected quantile loss, a popular loss function in quantile regression, to monitor the quantile tracking error, which is used to ...
Apr 27, 2020 · The experiments show that the procedures are highly efficient and track quantiles with an error close to the theoretical optimum. The Oracle ...
In this paper, we present a procedure that allows a quantile tracking procedure to cope with concept drift. We suggest using expected quantile loss, a popular ...
In this paper, we present a procedure that allows a quantile tracking procedure to cope with concept drift. We suggest using expected quantile loss, a popular ...
Apr 14, 2022 · In this paper, we present a procedure that allows a quantile tracking procedure to cope with concept drift. We suggest using expected quantile ...
Efficient quantile tracking using an oracle. Authors: H. L. Hammer, A. Yazidi, M. Riegler and H. Rue. Status: Published. Publication type: Journal Article.
For incremental quantile estimators the step size and possibly other tuning parameters must be carefully set. However, little attention has been given on ...
Apr 27, 2020 · We present a method that simultaneously maintain estimates of multiple quantiles of the data stream distribution. The method is based on making ...