Sequential Pattern Recognition: Naive Bayes Versus Fuzzy Relation Method

Author

Kurzynski, Marek ; Zolnierek, Andrzej

Author_Institution

Fac. of Electron., Wroclaw Univ. of Technol.

Volume

1

fYear

2005

fDate

28-30 Nov. 2005

Firstpage

1165

Lastpage

1170

Abstract

In this paper two possibilities of taking into account the dependencies in the sequential pattern recognition task are considered. The first method is naive Bayes attempt adopted to the probabilistic model of sequential decision problem in which, the assumption of Markov dependence in the sequence of recognized patterns is made. The second one is the fuzzy relation approach, in which we omitted such not necessary correct assumptions. Furthermore, both methods were applied to the medical diagnostic task and the results of computer investigations are discussed

Keywords

Bayes methods; Markov processes; decision theory; fuzzy set theory; medical diagnostic computing; pattern recognition; probability; Bayes method; Markov dependence; fuzzy relation method; medical diagnostic task; probabilistic model; sequential decision problem; sequential pattern recognition; Computer networks; Context modeling; Current measurement; Diseases; Fuzzy systems; Inference algorithms; Mathematical model; Medical diagnosis; Medical treatment; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on

Conference_Location

Vienna

Print_ISBN

0-7695-2504-0

Type

conf

DOI

10.1109/CIMCA.2005.1631420

Filename

1631420