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Инициализация полигармонического каскада, запуск и проверка
1. Baldi, P., Sadowski, P., Whiteson, D.: Searching for Exotic Particles in High-Energy Physics with Deep Learning. Nature Communications 5, 4308 (2014). https://doi.org/10.1038/ncomms5308
2. Бахвалов Ю. Н. 2025. Решение регрессионной задачи машинного обучения на основе теории случайных функций. PREPRINTS.RU. https://doi.org/10.24108/preprints-3113020
3. Бахвалов Ю. Н. 2025. Пакеты полигармонических сплайнов, их объединение, эффективные процедуры вычисления и дифференцирования. PREPRINTS.RU. https://doi.org/10.24108/preprints-3113111
4. Бахвалов Ю. Н. 2025. Полигармонический каскад. PREPRINTS.RU. https://doi.org/10.24108/preprints-3113501
5. Bookstein, F. L. (June 1989). "Principal warps: thin plate splines and the decomposition of deformations". IEEE Transactions on Pattern Analysis and Machine Intelligence. 11 (6): 567–585. doi:10.1109/34.24792
6. Delsarte, P., Goethals, J.-M., & Seidel, J. J. (1977). Spherical codes and designs. Geometriae Dedicata, 6, 363–388.
7. Dua, D., Graff, C.: UCI Machine Learning Repository (2019). University of California, Irvine, School of Information and Computer Sciences. [Доступ: July 31, 2025]. URL: https://archive.ics.uci.edu/ml/datasets/HIGGS
8. Golub, G. H., & Van Loan, C. F. (2013). Matrix Computations (4th ed.). Johns Hopkins University Press.
9. Hancock, John T., and Taghi M. Khoshgoftaar.(2020) "CatBoost for big data: an interdisciplinary review." Journal of big data 7, no. 1 : 94.
10. Harder R.L. and Desmarais R.N.: Interpolation using surface splines. Journal of Aircraft, 1972, Issue 2, pp. 189−191
11. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. DOI:10.1109/5.726791
12. McKay, M. D., Beckman, R. J., & Conover, W. J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 21, 239–245 (1979).
13. Miroslav Kubu, Petr Bour. Deep Learning in High Energy Physics. Stochastic and Physical Monitoring Systems. SPMS 2018.
14. Mourad Azhari, Abdallah Abarda, Badia Ettaki, Jamal Zerouaoui, and Mohamed Dakkon. Higgs boson discovery using machine learning methods with pyspark. Procedia Computer Science, 170:1141–1146,2020.
15. Niederreiter, H. Random Number Generation and Quasi-Monte Carlo Methods , volume 63 of SIAM CBMS-NSF Regional Conference Series in Applied Mathematics. SIAM, Philadelphia, PA, 1992.
16. Present by Anton Apostolatos and Leonard Bronner “Identifying the Higgs Boson with Convolutional Neural Networks”, in 2017.
17. Пугачев В.С., Теория случайных функций и её применение к задачам автоматического управления. Изд. 2-ое, перераб. и допол. — М.: Физматлит, 1960.
18. Ramakrishnan, U.; Nachimuthu, N. An Enhanced Memetic Algorithm for Feature Selection in Big Data Analytics with MapReduce. Intell. Autom. Soft Comput. 2022, 31, 1547–1559.
19. Wei Gao, Rong Jin, Shenghuo Zhu, and Zhi-Hua Zhou. One-pass auc optimization. In Proceedings of The 30th International Conference on Machine Learning, pages 906–914, 2013.
20. Zhang, H., Si, S., & Hsieh, C. J. (2017). GPU-acceleration for Large-scale Tree Boosting. arXiv preprint arXiv:1706.08359
21. PASCAL Network of Excellence. Epsilon Dataset (PASCAL Large Scale Learning Challenge 2008) [Data set; ID 45575, Version 1]. OpenML, 2008. URL: https://www.openml.org/d/45575
22. https://github.com/xolod7/polyharmonic-cascade.git
23. https://zenodo.org/records/16811633