Model selection in function approximation problem based on statistical learning theory

Autor: Burnaev, E. V., Belyaev, M. G.

In order to approximate multidimensional function it is necessary to select the complexity of the model used for approximation of unknown function. Method on basis of statistical learning theory for complexity estimation of a model is elaborated. Pro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1594::8f6e2c4dbb7782605a59d014d96c07e2
http://elib.bsu.by/handle/123456789/94592

Dimension reduction as a part of function approximation problem

Autor: Bernstein, A. V., Burnaev, E. V., Belyaev, M. G., Prihodko, P. V.

Usually in case of approximation of high-dimensional unknown dependencies the "curse of dimensionality" problem arises. In order to deal with such problem preliminary dimension reduction of input vector should be done. In this paper the methodology f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1594::dd53543b03b2e555fa023adcb566f4a6
http://elib.bsu.by/handle/123456789/94591

[Artificial intelligence for diagnosis of vertebral compression fractures using a morphometric analysis model, based on convolutional neural networks].

Autor: Petraikin AV; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies., Belaya ZE; Endocrinology Research Centre., Kiseleva AN; Research Center of Neurology., Artyukova ZR; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies., Belyaev MG; Skolkovo Institute of Science and Technology., Kondratenko VA; Skolkovo Institute of Science and Technology., Pisov ME; Skolkovo Institute of Science and Technology; Kharkevich Institute for Information Transmission Problems., Solovev AV; Sklifosovsky Clinical and Research Institute of Emergency Medicine., Smorchkova AK; Central State Medical Academy of the Presidential Administration of the Russian Federation., Abuladze LR; Sechenov University., Kieva IN; Peoples' Friendship University of Russia., Fedanov VA; Central State Medical Academy of the Presidential Administration of the Russian Federation., Iassin LR; Sechenov University., Semenov DS; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies., Kudryavtsev ND; Endocrinology Research Centre., Shchelykalina SP; Pirogov Russian National Research Medical University., Zinchenko VV; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies., Akhmad ES; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies., Sergunova KA; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies., Gombolevsky VA; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies., Nisovstova LA; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies., Vladzymyrskyy AV; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies., Morozov SP; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies.

Publikováno v: Problemy endokrinologii [Probl Endokrinol (Mosk)] 2020 Oct 24; Vol. 66 (5), pp. 48-60. Date of Electronic Publication: 2020 Oct 24.