Article

Article title OPERATIVE TEMPORAL PATTERNS RECOGNITION IN SEQUENTIAL DATA
Authors S.M. Kovalev, A.V. Muravskiy
Section SECTION III. ARTIFICIAL INTELLECT AND INDISTINCT SYSTEMS
Month, Year 07, 2012 @en
Index UDC 519.816
DOI
Abstract A new hybrid approach is developed in this article. The approach is based on adaptive stochastic models and is used for temporal patterns recognition in sequential data. The conditions connecting values of function of the validity of Markov model with probabilities of occurrence in data flows of target patterns are formulated Adaptive Markov model of temporal process is considered along with training method. The model is used for temporal evolutionary patterns prediction. The method of training of game models is developed. Generalisation of the offered approach on a case indistinct temporal patterns is given. The area of possible appendices of methods of anticipatory recognition and results of experiments is described.

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Keywords Game Markov model; temporal-difference training; temporal pattern.
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