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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dc.subject.en
Hierarchial Estimator
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dc.subject.en
hierarchical model
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dc.subject.en
regression
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dc.subject.en
function approximation
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dc.subject.en
classifier error
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dc.description.volume
20
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dc.identifier.doi
10.4467/20838476SI.11.004.0290
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dc.identifier.eissn
2083-8476
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dc.title.journal
Schedae Informaticae
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dc.language.container
eng
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dc.date.accession
2019-06-12
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dc.affiliation
Wydział Fizyki, Astronomii i Informatyki Stosowanej
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dc.subtype
Article
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dc.rights.original
OTHER; otwarte czasopismo; ostateczna wersja wydawcy; w momencie opublikowania; 0