The Empirical Study of the Naive Bayes Classifier in the Case of Markov Chain Recognition Task
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.
KeywordsMarkov Chain Feature Vector Simulation Investigation Pattern Recognition Algorithm Control Markov Chain
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- 3.Fu K (1974) Syntactic methods in pattern recognition. New York Academic PressGoogle Scholar
- 5.Kurzynski M (1997) Pattern recognition-statistical approach. Publishers of Wroclaw University of Technology.Google Scholar
- 7.McCallum, Nigam K (1998) A comparison of event models for naive Bayes text classication. In AAAI-98 Workshop on Learning and Text Categorization, Madison, WI, USA:41–48Google Scholar
- 8.Mitchel T (1997) Machine learning. McGraw Hill, New YorkGoogle Scholar
- 10.Zolnierek A (1982) Computer-aided recognition of the human acid-base state. In Proc. of 6-th Int. Conf. on Pattern Recognition:1219Google Scholar
- 12.Zolnierek A (2003) The simulation investigations of pattern recognition algorithm for second-order Markov chains. In: Proc. of the 37-th conference, Brno, Czech Republic, Acta MOSIS 92:29–35Google Scholar