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The regular immune function regression of mammalian females and their interaction in urban habitat
Malikovich A., Nafisovich M., Nizamutdinovich S.
system, there is multiple, rather constant, immune quality functions regression (algorithm), which
Comparison of generalization error's methods on case of linear regression
Pavel, Peresunko, Kristina, Pakhomova, Eugenia, Soroka, Sergey, Videnin
to samples and feature count of dataset. This research shows accuracy of each method for Lasso regression
Determinants Factors in Predicting Life Expectancy Using Machine Learning
Kouame Amos B., Smirnov I.V.
coefficient to assess correlations between indicators, and we use multiple linear regression models, Ridge
Identification of bots in social networks based on data mining technologies
Ismagilov I.I., Sabirova A.I., Kataseva D.V., Katasev A.S.
and linear regression are performed. The initial data source was Twitter social network. To collect
ANALYSIS AND SYNTHESIS OF THE CHEBYSHEV POLYNOMIALS IN THE REGRESSION ANALYSIS PROBLEMS
Isaev A.B., Kovalchukov N.N., Saveliev I.A.
relationships. The authors prove the theories of linear span properties, polynomial regression space
Macroeconomic determinants of the mortgage loan
Kadochnikova E., Bulatova E., Safiullina A., Suycheva D.
Linear regression model
Generalized Embedding Regression: A Framework for Supervised Feature Extraction
Lu J., Lai Z., Wang H., Chen Y., Zhou J., Shen L.
performance in recognition tasks. In this article, a framework called generalized embedding regression (GER
Physical-chemical predictors of soil pollution in the megapolis
Azovtseva N., Smagin A., Sadovnokova N., Kirichenko A., Lasareva E.
density. Multiple regression (R2=0.91-0.95) of layered heavy metals, as well as accumulation of biophilic
Numerical study of free vibration behaviour of filled tool holder using epoxy-granite
Kashyzadeh K.R., Ghorbani S.
in diameter and length will reduce the natural frequency. Moreover, Multiple Regression Technique (MRT
The integrals and integral transformations connected with the joint vector Gaussian distribution
Mukha, V. S., Kako, N. F.
of the coefficients of the multiple regression function are obtained. The proposed integrals can be used as table

Страница 8 из 1792

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