On Mean Squared Error of Hierarchical Estimator
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Publication date: 23.01.2012
Schedae Informaticae, 2011, Volume 20, pp. 83 - 99
https://doi.org/10.4467/20838476SI.11.004.0290Authors
On Mean Squared Error of Hierarchical Estimator
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.
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Information: Schedae Informaticae, 2011, Volume 20, pp. 83 - 99
Article type: Original article
Titles:
On Mean Squared Error of Hierarchical Estimator
On Mean Squared Error of Hierarchical Estimator
Jagiellonian University, Faculty of Physics, Astronomy and Applied Computer Science, Cracow, Poland
Published at: 23.01.2012
Article status: Open
Licence: None
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English