@article{Guo2023, 
author = {Qin Guo and Binlei Cai},
title = {Learning capability of the rescaled pure greedy algorithm with non-iid sampling},
year = {2023},
journal = {Electronic Research Archive},
volume = {31},
number = {3},
pages = {1387-1404},
keywords = {rescaled pure greedy algorithm, β-mixing, non-identical sequences, drift error, covering number, learning rate},
url = {https://www.sciopen.com/article/10.3934/era.2023071},
doi = {10.3934/era.2023071},
abstract = {We consider the rescaled pure greedy learning algorithm (RPGLA) with the dependent samples drawn according to a non-identical sequence of probability distributions. The generalization performance is provided by applying the independent-blocks technique and adding the drift error. We derive the satisfactory learning rate for the algorithm under the assumption that the process satisfies stationary    β-mixing, and also find that the optimal rate    O  (      n          −      1        ) can be obtained for i.i.d. processes.}
}