PL EN


Preferences help
enabled [disable] Abstract
Number of results
2011 | 20 |
Article title

On Mean Squared Error of Hierarchical Estimator

Content
Title variants
Languages of publication
PL
Abstracts
PL
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.
Publisher
Year
Volume
20
Physical description
Dates
published
2011
online
21 - 05 - 2015
Contributors
References
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.ojs-issn-2083-8476-year-2011-volume-20-article-2224
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.