Statistical Recognition Method Based on Nonlinear Regression

M. B. Gavrikov and N. V. Pestryakova, “A polynomial regression method for recognition of printed and handwritten characters,” Prepr. Inst. Prikl. Mat. im. M. V. Keldysha, No. 30 (2004). M. B. Gavrikov, A. V. Misyurev, N. V. Pestryakova, and O. A. Slavin, “On a pattern recognition method based on polynomial regression,” Autom. Remote Control 67 (2), 278–292 (2006). N. V. Pestryakova, A Character Recognition Method Based on Polynomial Regression (URSS, Moscow, 2011) [in Russian]. M. B. Gavrikov, N. V. Pestryakova, and B. M. Gavrikov, “On effective applications of polynomial regression to the problem of character recognition,” Tr. Inst. Sist. Anal. Ross. Akad. Nauk 64 (4), 89–96 (2014). B. M. Gavrikov, M. B. Gavrikov, and N. V. Pestryakova, “Statistical analysis of characteristics of the recognition method on the training set,” Inf. Tekhnol. Vychisl. Sist., No. 2, 50–67 (2015). B. M. Gavrikov, M. B. Gavrikov, I. M. Lebedenko, N. V. Pestryakova, and R. V. Stavitskii, “A method for human health assessment,” Prepr. Inst. Prikl. Mat. im. M. V. Keldysha, No. 8 (2017). B. M. Gavrikov, M. B. Gavrikov, N. V. Pestryakova, and R. V. Stavitskii, “The structure of the training base for the statistical classifier of the states of human body systems,” Prepr. Inst. Prikl. Mat. im. M. V. Keldysha, No. 255 (2018). V. V. Arlazarov, A. E. Zhukovskii, V. E. Krivtsov, D. P. Nikolaev, and D. V. Polevoi, “Analysis of the features of using stationary and mobile small-sized digital video cameras for document recognition,” Inf. Tekhnol. Vychisl. Sist., No. 3, 71–81 (2014). O. O. Petrova and K. B. Bulatov, “Methods for post-processing of the results of recognition of the machine-readable zone of documents,” Tr. Inst. Sist. Anal. Ross. Akad. Nauk, Suppl., 43–50 (2018). V. V. Arlazarov, K. B. Bulatov, and A. V. Uskov, “A model of an object recognition system in the video stream of a mobile device,” Tr. Inst. Sist. Anal. Ross. Akad. Nauk, Suppl., 73–82 (2018). R. V. Stavitskii, L. A. Lebedev, A. L. Lebedev, and A. Yu. Smyslov, Quantification of the Homeostatic Activity of Healthy and Diseased Individuals (GART, Moscow, 2013) [in Russian]. N. Yu. Dobrovol’skaya, L. A. Lebedev, A. L. Lebedev, Yu. B. Novozhilov, and R. V. Stavitskii, “Chemo-radiation therapy for cervical cancer. Methodology for assessing the state of the body and its systems,” Radiol.-Prakt., No. 3, 53–63 (2011). I. M. Lebedenko, T. Z. Chernyavskaya, R. V. Stavitskii, and O. N. Plautin, “Technical control of the state of the body and its systems in the process of chemotherapy and radiation therapy and bone marrow transplantation in acute leukemia,” Med. Tekh., No. 5, 32–36 (2014). Yu. A. Tsybul’skaya, R. V. Stavitskii, I. M. Lebedenko, S. V. Smerdin, I. V. Shutikhina, L. S. Kokov, and O. V. Baturin, “A quantitative approach to the diagnosis of damage to the osteoarticular system in tuberculous spondylitis,” Med. Vestn. Sev. Kavk., No. 3, 212–217 (2015). A. B. Merkov, Pattern Recognition. Introduction to Statistical Learning Methods (URSS, Moscow, 2019) [in Russian]. A. B. Merkov, Pattern Recognition. Construction and Training of Probabilistic Models (URSS, Moscow, 2014) [in Russian]. K. V. Vorontsov, Mathematical Methods of Teaching by Precedents. Course of Lectures (Mosk. Fiz. Tekh. Inst., Dolgoprudny, 2012). http://www.machinelearning.ru/wiki/index.php?title=Машинное_обучение_(курс_лекций,_К.В.Воронцов). Yu. V. Linnik, The Method of Least Squares and the Foundations of the Mathematical-Statistical Theory of Observations Processing (Fizmatlit, Moscow, 1958) [in Russian]. M. B. Gavrikov and O. V. Lokutsievskii, Early Numerical Analysis (Yanus, Moscow, 1995) [in Russian]. J. Schürmann, Pattern Classification (Wiley, New York, 1996).