title: On the influence of the cental trend on the nature of the density distribution of maximum entropy in machine learning
metadata.name: dc.contributor.author
metadata.value: Kvasnov, Anton V.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Baranenko, Anatoliy A.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Butyrsky, Evgeniy Y.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Zaranik, Uliana P.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2023-08-22T10:24:49Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2023-08-22T10:24:49Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2023-06
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Kvasnov A. V., Baranenko A. A., Butyrsky E. Y., Zaranik U. P. On the influence of the cental trend on the nature of the density distribution of maximum entropy in machine learning. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2023, vol. 19, iss. 2, pp. 176–184.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu10.2023.204
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/43826
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The principle of maximum entropy (ME) has a number of advantages that allow it to be used in machine learning. The density distribution of maximum entropy (WEO) requires solving the problem of calculus of variations on the a priori distribution, where the central tendency can be used as a parameter. In Lebesgue space, the central tendency is described by the generalized Gelder average. The paper shows the evolution of the density of the ME distribution depending on the given norm of the average. The minimum Kulback — Leibler divergence between the WEO and the a prior density is achieved at the harmonic mean, which is effective in reducing the dimensionality of the training sample. At the same time, this leads to a deterioration in the function of loss in the conditions of machine learning by precedents.
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. Applied Mathematics. Computer Science. Control Processes;Volume 19; Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: maximum entropy principle
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: maximum entropy distribution
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: central trend
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: generalized average
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: machine learning
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: On the influence of the cental trend on the nature of the density distribution of maximum entropy in machine learning
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 04.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/43826/1/04.pdf
resources.format: Adobe PDF
resources.size: 385,6 kB