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On the mean squared error of Hierarchical Estimator
Hierarchial Estimator
hierarchical model
regression
function approximation
classifier error
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.
cris.lastimport.scopus | 2024-04-07T17:11:08Z | |
cris.lastimport.wos | 2024-04-10T00:14:27Z | |
dc.abstract.en | 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. | pl |
dc.affiliation | Wydział Fizyki, Astronomii i Informatyki Stosowanej | pl |
dc.contributor.author | Brodowski, Stanisław - 105941 | pl |
dc.date.accession | 2019-06-12 | pl |
dc.date.accessioned | 2019-06-12T06:43:59Z | |
dc.date.available | 2019-06-12T06:43:59Z | |
dc.date.issued | 2011 | pl |
dc.date.openaccess | 0 | |
dc.description.accesstime | w momencie opublikowania | |
dc.description.physical | 83-99 | pl |
dc.description.version | ostateczna wersja wydawcy | |
dc.description.volume | 20 | pl |
dc.identifier.doi | 10.4467/20838476SI.11.004.0290 | pl |
dc.identifier.eissn | 2083-8476 | pl |
dc.identifier.issn | 1732-3916 | pl |
dc.identifier.project | ROD UJ / OP | pl |
dc.identifier.uri | https://ruj.uj.edu.pl/xmlui/handle/item/77028 | |
dc.identifier.weblink | http://www.ejournals.eu/Schedae-Informaticae/2011/Volume-20/art/1207/ | pl |
dc.language | eng | pl |
dc.language.container | eng | pl |
dc.rights | Dozwolony użytek utworów chronionych | * |
dc.rights.licence | OTHER | |
dc.rights.uri | http://ruj.uj.edu.pl/4dspace/License/copyright/licencja_copyright.pdf | * |
dc.share.type | otwarte czasopismo | |
dc.subject.en | Hierarchial Estimator | pl |
dc.subject.en | hierarchical model | pl |
dc.subject.en | regression | pl |
dc.subject.en | function approximation | pl |
dc.subject.en | classifier error | pl |
dc.subtype | Article | pl |
dc.title | On the mean squared error of Hierarchical Estimator | pl |
dc.title.journal | Schedae Informaticae | pl |
dc.type | JournalArticle | pl |
dspace.entity.type | Publication |
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