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The Empirical Study of the Naive Bayes Classifier in the Case of Markov Chain Recognition Task

  • Andrzej Zolnierek
  • Bartlomiej Rubacha
Part of the Advances in Soft Computing book series (AINSC, volume 30)

Abstract

In this paper the problems of sequential pattern recognition are considered. As a statistical model of dependence, in the sequences of patterns, the firstorder Markov chain is assumed. Additionally, the assumption about independence between the attributes in the feature vector is made. The pattern recognition algorithms with such assumption are called in the literature “naive Bayes algorithm”. In this paper such approach is made to the pattern recognition algorithm for first-order Markov chain and some results of numerical investigation are presented. The main goal of these investigations was to verify if it is reasonable to make such assumption in the real recognition tasks.

Keywords

Markov Chain Feature Vector Simulation Investigation Pattern Recognition Algorithm Control Markov Chain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Andrzej Zolnierek
    • 1
  • Bartlomiej Rubacha
    • 1
  1. 1.Wroclaw University of TechnologyWroclawPoland

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