Abstract
The classical learning problem of the pattern recognition in a finite-dimensional linear space of real-valued features is studied under the conditions of a non-stationary universe. The training criterion of non-stationary pattern recognition is formulated as a generalization of the classical Support Vector Machine. The respective numerical algorithm has the computation complexity proportional to the length of the training time series.
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Krasotkina, O.V., Mottl, V.V., Turkov, P.A. (2011). Bayesian Approach to the Pattern Recognition Problem in Nonstationary Environment. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2011. Lecture Notes in Computer Science, vol 6744. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21786-9_6
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DOI: https://doi.org/10.1007/978-3-642-21786-9_6
Publisher Name: Springer, Berlin, Heidelberg
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