Fuzzy Observable Markov Models for Pattern Recognition | Zendy

Dat Tran | Zendy; Wanli Ma | Zendy; Dharmendra Sharma | Zendy

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Fuzzy Observable Markov Models for Pattern Recognition

Author(s) -

Dat Tran,

Wanli Ma,

Dharmendra Sharma

Publication year - 2007

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2007.p0662

Subject(s) - hidden markov model , computer science , observable , artificial intelligence , fuzzy logic , markov model , vector quantization , markov chain , gaussian , pattern recognition (psychology) , markov process , quantization (signal processing) , machine learning , algorithm , mathematics , statistics , physics , quantum mechanics

This paper presents a mathematical framework for fuzzy discrete and continuous observable Markov models (OMMs) and their applications to written language, spam email and typist recognition. Experimental results show that the proposed OMMs are more effective than models such as vector quantization, Gaussian mixture model and hidden Markov model.

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