|
|
|
Publications in Math-Net.Ru |
Citations |
|
2022 |
| 1. |
N. S. Korolev, O. V. Senko, “Method for improving gradient boosting learning efficiency based on modified loss functions”, Avtomat. i Telemekh., 2022, no. 12, 78–88  ; Autom. Remote Control, 83:12 (2022), 1935–1943 |
1
|
| 2. |
O. V. Sen'ko, A. V. Kuznetsova, E. M. Voronin, O. A. Kravtsova, L. R. Borisova, I. L. Kirilyuk, V. G. Akimkin, “Methods of intellectual data analysis in COVID -19 research”, Journal of the Belarusian State University. Mathematics and Informatics, 1 (2022), 83–96 |
|
2021 |
| 3. |
Yu. I. Zhuravlev, O. V. Sen'ko, A. A. Dokukin, N. N. Kiselyova, I. A. Saenko, “Two-level regression method using ensembles of trees with optimal divergence”, Dokl. RAN. Math. Inf. Proc. Upr., 499 (2021), 63–66   ; Dokl. Math., 104:1 (2021), 212–215  |
7
|
| 4. |
I. L. Kirilyuk, A. V. Kuznetsova, O. V. Senko, “Investigation of the relationship between production functions and socio-economic indicators of Russian regions by the method of optimal partitioning”, Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2021, no. 1, 20–31 |
1
|
| 5. |
A. N. Mazilina, O. V. Senko, O. S. Brusov, A. A. Dokukin, M. S. Kodryan, A. V. Kuznetsova, L. L. Klimenko, “Determining subgroups of significant correlation in analyzing relation between NR2 antibodies and factor VIII in acute neurological diseases”, Mat. Biolog. Bioinform., 16:1 (2021), 29–38 |
1
|
|
2020 |
| 6. |
I. L. Kirilyuk, O. V. Sen'ko, “Assessing the validity of clustering of panel data by Monte Carlo methods (using as example the data of the Russian regional economy)”, Computer Research and Modeling, 12:6 (2020), 1501–1513 ; Computer Research and Modeling, 12:6 (2020), e1501–e1513 |
3
|
| 7. |
L. R. Borisova, A. V. Kuznetsova, N. V. Sergeeva, O. V. Sen'ko, “Comparison of Arctic zone RF companies with different polar index ratings by economic criteria with the help of machine learning tools”, Computer Research and Modeling, 12:1 (2020), 201–215 |
2
|
| 8. |
I. L. Kirilyuk, O. V. Sen'ko, “Selection of optimal complexity models by methods of nonparametric statistics (on the example of production function models of the regions of the Russian Federation)”, Inform. Primen., 14:2 (2020), 111–118 |
2
|
|
2019 |
| 9. |
Yu. I. Zhuravlev, O. V. Sen'ko, N. N. Bondarenko, V. V. Ryazanov, A. A. Dokukin, A. P. Vinogradov, “Research of the possibility to forecast changes in financial state of a credit organization on the basis of public financial statements”, Inform. Primen., 13:4 (2019), 30–35 |
2
|
| 10. |
I. V. Dorovskih, O. V. Sen'ko, V. Ya. Chuchupal, A. A. Dokukin, A. V. Kuznetsova, “On possibility of machine learning application for diagnosing dementia by EEG signals”, Mat. Biolog. Bioinform., 14:2 (2019), 543–553 |
2
|
| 11. |
I. S. Ozhereliev, O. V. Senko, N. N. Kiseleva, “Method for searching outlier objects using parameters of learning instability”, Sistemy i Sredstva Inform., 29:2 (2019), 122–134 |
2
|
|
2018 |
| 12. |
O. V. Senko, M. S. Kodryan, A. V. Kuznetsova, L. L. Klimenko, A. I. Deev, I. S. Baskakov, A. N. Mazilina, “Optimal partitioning method for evaluating of effect of hemoglobin oxygenation levels of vessel endothelial growth factor”, Mat. Biolog. Bioinform., 13:2 (2018), 563–590 |
3
|
|
2017 |
| 13. |
O. V. Senko, V. Ya. Chuchupal, A. A. Dokukin, “Non-invasive arterial pressure estimating with the cardiac monitor CardioQvark”, Mat. Biolog. Bioinform., 12:2 (2017), 536–545 |
4
|
|
2016 |
| 14. |
R. R. Guliev, O. V. Senko, D. A. Zateishchikov, V. V. Nosikov, I. V. Uporov, A. V. Kuznetsova, M. A. Evdokimova, S. N. Tereshchenko, E. V. Akatova, M. G. Glaser, A. S. Galyavich, N. A. Koziolova, A. V. Yagoda, O. I. Boeva, S. V. Shlyk, S. Yu. Levashov, V. O. Konstantinov, V. A. Brazhnik, S. D. Varfolomeev, I. N. Kurochkin, “The use of optimal partitionings for multiparameter data analysis in clinical trials”, Mat. Biolog. Bioinform., 11:1 (2016), 46–63 |
8
|
|
2015 |
| 15. |
I. L. Kirilyuk, A. I. Volynsky, M. S. Kruglova, A. V. Kuznetsova, A. A. Rubinstein, O. V. Sen'ko, “Empirical testing of institutional matrices theory by data mining”, Computer Research and Modeling, 7:4 (2015), 923–939 |
3
|
| 16. |
A. A. Dokukin, O. V. Senko, “Regression model based on convex combinations best correlated with response”, Zh. Vychisl. Mat. Mat. Fiz., 55:3 (2015), 530–544  ; Comput. Math. Math. Phys., 55:3 (2015), 526–539  |
2
|
|
2011 |
| 17. |
A. V. Kuznetsova, I. V. Kostomarova, N. N. Vodolagina, N. A. Malygina, O. V. Senko, “Study of effects of clinical and genetic factors on severity of discirculatory encephalopathy with the help of pattern recognition methods”, Mat. Biolog. Bioinform., 6:1 (2011), 115–146 |
3
|
| 18. |
A. A. Dokukin, O. V. Senko, “Optimal convex correcting procedures in problems of high dimension”, Zh. Vychisl. Mat. Mat. Fiz., 51:9 (2011), 1751–1760 ; Comput. Math. Math. Phys., 51:9 (2011), 1644–1652 |
4
|
|
2003 |
| 19. |
O. V. Sen'ko, “A permutation test in the optimal partition method”, Zh. Vychisl. Mat. Mat. Fiz., 43:9 (2003), 1422–1431 ; Comput. Math. Math. Phys., 43:9 (2003), 1368–1376 |
|
1995 |
| 20. |
O. V. Sen'ko, “Application of a weighted voting procedure in a system of base sets in prediction problems”, Zh. Vychisl. Mat. Mat. Fiz., 35:10 (1995), 1552–1563 ; Comput. Math. Math. Phys., 35:10 (1995), 1249–1257 |
|
1992 |
| 21. |
O. V. Sen'ko, “Estimate of the a posteriori probabilities of objects belonging to classes in the case of a voting procedure”, Zh. Vychisl. Mat. Mat. Fiz., 32:4 (1992), 635–644 ; Comput. Math. Math. Phys., 32:4 (1992), 543–551 |
1
|
|
|
|
2013 |
| 22. |
A. V. Kuznetsova, I. V. Kostomarova, O. V. Senko, “Logical and Statistical Analysis of Relationship Between Clinical and Laboratory Indices and Disturbances of Cerebral Blood Circulation in Elderly Patients with Chronic Ischemia of the Brain”, Mat. Biolog. Bioinform., 8:1 (2013), 182–224 |
2
|
|
| Organisations |
|
| |
|
|
Contact us: