Bayesian Approach to the Pattern Recognition Problem in Nonstationary Environment

  • O. V. Krasotkina
  • V. V. Mottl
  • P. A. Turkov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)


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.


  1. 1.
    Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23(1), 69–101 (1996)Google Scholar
  2. 2.
    Salganicoff, M.: Tolerating concept and sampling shift in lazy learning using prediction error context switching. AI Review, Special Issue on Lazzy Learning 11(1-5), 133–155 (1997)Google Scholar
  3. 3.
    Klinkenberg, R.: Learning drifting concepts example selection vs. example weighting. Intelligent data analysis, Special Issue on Incremental Learning Systems Capable of Dealing with Concept Drift 8(3) (2004)Google Scholar
  4. 4.
    Harries, M., Sammut, C., Horn, K.: Extracting hidden context. Machine Learning 32(2), 101–126 (1998)CrossRefzbMATHGoogle Scholar
  5. 5.
    Muhlbaier, M.D., Polikar, R.: An Ensemble Approach for Incremental Learning in Nonstationary Environments. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 490–500. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Tatarchuk, A.I., Sulimova, V.V., Mottl, V.V., Windridge, D.: Method of relevant potential functions for selective combination of diverse information in the pattern recognition learning based on Bayesian approach. In: MMRO-14: Conf. Proc., Suzdal, pp. 188–191 (2009)Google Scholar
  7. 7.
    Ma, J., Saul, K.L., Savage, S., Voelker, G.: Identifying Suspicious URLs: An Application of Large-Scale Online Learning. In: Proceedings of the International Conference on Machine Learning (ICML), Montreal, Quebec, June 2009, pp. 681–688 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • O. V. Krasotkina
    • 1
  • V. V. Mottl
    • 2
  • P. A. Turkov
    • 1
  1. 1.Tula State UniversityTulaRussia
  2. 2.Computing Centre of RASRussia

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