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

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Book cover Computer Recognition Systems

Part of the book series: Advances in Soft Computing ((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.

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Author information

Authors and Affiliations

  1. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Andrzej Zolnierek (Chair of Systems and Computer Networks) & Bartlomiej Rubacha

Authors
  1. Andrzej Zolnierek
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  2. Bartlomiej Rubacha
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Editor information

Editors and Affiliations

  1. Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Marek Kurzyński , Edward Puchała , Michał Woźniak  & Andrzej żołnierek , ,  & 

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© 2005 Springer-Verlag Berlin Heidelberg

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Zolnierek, A., Rubacha, B. (2005). The Empirical Study of the Naive Bayes Classifier in the Case of Markov Chain Recognition Task. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_38

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  • DOI: https://doi.org/10.1007/3-540-32390-2_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25054-8

  • Online ISBN: 978-3-540-32390-7

  • eBook Packages: EngineeringEngineering (R0)

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