@article{9c544317-c04f-4fbf-bab2-40323e44a58d,
author = {Stanisław Brodowski},
title = {On Mean Squared Error of Hierarchical Estimator},
journal = {Schedae Informaticae},
volume = {2011},
number = {Volume 20},
year = {2012},
issn = {1732-3916},
pages = {83-99},keywords = {Hierarchial Estimator; hierarchical model; regression; function approximation; error; theorem},
abstract = {In this paper a new theorem about components of the mean squared error of Hierarchical Estimator is presented. Hierarchical Estimator is a machine learning meta-algorithm that attempts to build, in an incremental and hierarchical manner, a tree of relatively simple function estimators and combine their results to achieve better accuracy than any of the individual ones. The components of the error of a node of such a tree are: weighted mean of the error of the estimator in a node and the errors of children, a non-positive term that descreases below 0 if children responses on any example dier and a term representing relative quality of an internal weighting function, which can be conservatively kept at 0 if needed. Guidelines for achieving good results based on the theorem are brie discussed.},
doi = {10.4467/20838476SI.11.004.0290},
url = {https://ejournals.eu/en/journal/schedae-informaticae/article/on-mean-squared-error-of-hierarchical-estimator}
}