|
|
|
References
|
|
|
1. |
Aivazyan S. A., Enyukov I. S., Meshalkin L. D., Issledovanie zavisimostei, Finansy i statistika, M., 1988 |
2. |
Pogozhev I. B., Primenenie matematicheskikh modelei zabolevanii v klinicheskoi praktike, Nauka, M., 1988 |
3. |
Gallager R., Teoriya informatsii i nadezhnaya svyaz, Sov. radio, M., 1974 |
4. |
Suzuki T., Sugiyama M., Kanamori T., Sese J., “Mutual information estimation reveals global associations between stimuli and biological processes”, BMC Bioinformatics, 10, Suppl. 1 (2009), S52 |
5. |
Vergara J. R., Estevez P. A., “A review of feature selection methods based on mutual information”, Neural Comput & Applic., 24 (2014), 175–186 |
6. |
Vapnik V., Izmailov R., “Statistical inference problems and their rigorous solutions”, Statistical Learning and Data Sciences, SLDS Lecture Notes in Computer Science, 9047, eds. Gammerman A., Vovk V., Papadopoulos H., 2015, 33–75 |
7. |
Gine E., Nickl R., Mathematical Foundations of Infinite-Dimensional Statistical Model, Cambridge Academ., 2015 |
8. |
Manton J. H., Amblard P.-O., “A Primer on Reproducing Kernel Hilbert Spaces”, Foundations and Trends in Signal Processing, 8:1 (2014), 1–26 |
9. |
Scholkopf B., Herbrich R., Smola A. J., “A generalized representer theorem”, Proceedings of the 14th Annual Conference on Computational Learning Theory and 5th European Conference on Computational Learning Theory, COLT'01/EuroCOLT'01, 2001, 416–426 |
10. |
Aizerman M. A., Braverman E. M., Rozonoer L. I., Metod potentsialnykh funktsii v teorii obucheniya mashin, Nauka, M., 1970 |
11. |
Mikhalskii A. I., Petrov I. V., Tsurko V. V., et al., “Application of mutual information estimation for predicting the structural stability of pentapeptides”, Russ. J. Numer. Anal M., 35:5 (2020), 263–271 |
12. |
Nekrasov A., “Entropy of Protein Sequences: An Integral Approach”, J. Biomolec. Structur. Dynam., 20 (2002), 87–92 |
13. |
Nekrasov A., Alekseeva L., Pogosyan R., et al., “A minimum set of stable blocks for rational design of polypeptide chains”, Biochimie, 160 (2019), 88–92 |
14. |
Breiman L., “Random Forests”, Machin. Learning J., 45:1 (2001), 5–32 |