title: On the choice of basic regression functions and machine learning
metadata.name: dc.contributor.author
metadata.value: Ermakov, Sergey M.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Leora, Svetlana N.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2022-04-14T10:54:14Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2022-04-14T10:54:14Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2022-03
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Ermakov S.M., Leora S.N. On the choice of basic regression functions and machine learning. Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy, 2022, vol. 9 (67), issue 1, pp. 11–22.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu01.2022.102
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/36150
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: As is known, the regression analysis task is widely used in machine learning problems, which allows to establish relationship between observed data and compactly store of information. Most often, a regression function is described by a linear combination of some of the selected functions fj (X), j = 1, . . . ,m, X 2 D ⊂ Rs. If the observed data contains a random error, then the regression function restored from the observed data contains a random error and a systematic error depending on the selected functions fj . The article indicates the possibility of optimal selection of functions fj in the sense of a given functional metric, if it is known that the true dependence is consistent with some functional equation. In some cases (regular grids, s ≤ 2), similar results can be obtained using the random process analysis method. The numerical examples given in this article illustrate much more opportunities for the task of constructing the regression function.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Mathematics. Mechanics. Astronomy;Volume 9; Issue 1
metadata.lang: -
metadata.name: dc.subject
metadata.value: regression analysis
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: approximation
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: basis functions
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: operator method
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: machine learning
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: On the choice of basic regression functions and machine learning
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 11-22.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/36150/1/11-22.pdf
resources.format: Adobe PDF
resources.size: 598,79 kB