On mean squared error of Hierarchical Estimator

• Autorzy: Brodowski S.
• Języki publikacji: EN

Abstrakty

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 decreases 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 y discussed.

Słowa kluczowe

EN

Hierarchial Estimator; hierarchical model; regression; classifier error

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2011
• Tom: Vol. 20
• Strony: 83--99
• Opis fizyczny: Bibliogr. 18 poz.

Twórcy

autor

Brodowski S.

• Faculty of Physics, Astronomy, and Applied Computer Science, Jagiellonian University, Reymonta 4, 30-059 Kraków, Poland,

Bibliografia

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