Fuzzy Observable Markov Models for Pattern Recognition

    Research output: Contribution to journalArticle

    Abstract

    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
    Original languageEnglish
    Pages (from-to)662-667
    Number of pages6
    JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
    Volume11
    Issue number6
    DOIs
    Publication statusPublished - 2007

    Fingerprint

    Pattern recognition
    Vector quantization
    Electronic mail
    Hidden Markov models

    Cite this

    @article{351f06763de04d76987fe6ab3c9cc380,
    title = "Fuzzy Observable Markov Models for Pattern Recognition",
    abstract = "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",
    author = "Dat Tran and Wanli Ma and Dharmendra Sharma",
    year = "2007",
    doi = "10.20965/jaciii.2007.p0662",
    language = "English",
    volume = "11",
    pages = "662--667",
    journal = "Journal of Advanced Computational Intelligence and Intelligent Informatics",
    issn = "1343-0130",
    publisher = "Fuji Technology Press",
    number = "6",

    }

    TY - JOUR

    T1 - Fuzzy Observable Markov Models for Pattern Recognition

    AU - Tran, Dat

    AU - Ma, Wanli

    AU - Sharma, Dharmendra

    PY - 2007

    Y1 - 2007

    N2 - 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

    AB - 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

    U2 - 10.20965/jaciii.2007.p0662

    DO - 10.20965/jaciii.2007.p0662

    M3 - Article

    VL - 11

    SP - 662

    EP - 667

    JO - Journal of Advanced Computational Intelligence and Intelligent Informatics

    JF - Journal of Advanced Computational Intelligence and Intelligent Informatics

    SN - 1343-0130

    IS - 6

    ER -