Source: http://iitp.ru/ru/userpages/125/
Timestamp: 2019-04-24 00:55:40+00:00

Document:
of the Aggregation of Specialized Forecast Algorithms // Journal of Communications Technology and Electronics, 2016, Vol. 61, No. 12, pp. 1400–1410.
V"yugin V.V., Trunov V.G. Adaptive Universal Marketing Strategy. Journal of Communications Technology and Electronics, 2015, Vol. 60, No. 6, pp. 658–672.
Vladimir V. V’yugin, Vladimir G. Trunov, Universal algorithmic trading. Journal of Investment Strategies Volume 2/Number 1, Winter 2012/13 P.63–88.
Vladimir V. V"yugin.. Universal Algorithm for Trading in Stock Market Based on the Method of Calibration.Lecture Notes in Artificial Intelligence (LNAI). 8139, P.53-67 (2013).
Vladimir V. V"yugin Online Learning in Case of Unbounded Losses Using Follow the Perturbed Leader Algorithm // Journal of Machine Learning Research V.12. P. 241−266, 2011.
Vladimir V. V"yugin On Empirical Meaning of Randomness with Respect to Parametric Families of Probability Distributions // Theory of Computing Systems: Volume 50, Issue 2 (2012), P.296-312.
Vladimir G. Vovk, Vladimir V. V’yugin. On the empirical validity of the Bayesian method. Journal of the Royal Statistical Society B, 1993, v.55, N1, p.253--266.
Vladimir G. Vovk V.G., Vladimir V. V’yugin. Prequential Level of impossibility. Journal of the Royal Statistical Society B, 1994, v.56, N1, p.115--123.
V’yugin V.V. Algorithmic Entropy (complexity) of Finite Objects and its Application to Defining Randomness and Amount of Information // Selecta Mathematica formerly Sovietica, 1994, v.13, N4, p.357--389.
Vladimir V. V’yugin. Bayesianism: an algorithmic analysis. Information and Computation, 1996, v.127, N1, p.1--10.
Vladimir V. V’yugin. Ergodic theorems for individual random sequences. Theoretical Computer Science, 1998, v.207, N4, p.343-361.
Vladimir V. V’yugin. Non-stochastic infinite and finite sequences. Theoretical Computer Science, 1998, v.207, N4, p.363-382.
Vladimir V. V’yugin. Algorithmic complexity and stochastic properties of finite binary sequences. The Computer Journal, 1999, v.42, N4, p.294--317.
Vladimir V. V’yugin. Most Sequences are Stochastic Information and Computation, 2001, v.168, p.1-12.
Michael V. Vyugin, Vladimir V. V’yugin. Non-linear inequalities between Kolmogorov and predictive complexities. Proceedings of Twelfth Internatiolal Conference on Algorithmic Learning Theory -- ALT"01", 2001, pp.190-204.
Michael V. Vyugin., Vladimir V. V’yugin. Predictive complexity and information. Proceedingsof Fifteen International Conference on Computational Learning Theory -- COLT"02 Lecture Notes on Artificial Intelligence, 2375, 2002, pp.90-104.
Vladimir V. V’yugin. Suboptimal measures of predictive complexity for absolute loss function. Information and Computation, 175, 2002, pp.146-157.
Michael V. Vyugin, Vladimir V. V’yugin. On complexity of easy predictable sequences. Information and Computation 178, 2002, pp.241—252.
Lyubetsky V.A., V’yugin V.V. Methods of horizontal gene transfer determination using phylogenetic data // In Silico Biology (An International Journal on Computational Molecular Biology), V. 3, No 1,2, 2003, p. 17-31 (the computer version http://www.bioinfo.de/isb/2003/03/0003).
Ilya Nuretdinov, Vladimir V"yugin, AlexanderGammerman. Transductive Confidence Machine is Universal. Lecture Notes in Computer Science, Algorithmic Learning Theory, 2842, Springer, Heidelberg, 2003. P. 283-297.
Michael V. Vyugin, Vladimir V. V"yugin. Predictive complexity and information, Journal of Computer and System Sciences, v.70, 2005, N4, p.539-554.
Vladimir V. V"yugin. On Calibration Error of Randomized Forecasting Algorithms. Theoretical Computer Science 410 (2009), P. 1781-1795. 2009.

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