Fuzzy Subspace Hidden Markov Models for Pattern Recognition

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    Abstract

    This paper presents a novel fuzzy subspace-based approach to hidden Markov model. Features extracted from patterns are considered as feature vectors in a multi-dimensional feature space. Current hidden Markov modeling techniques treat features equally, however this assumption may not be true. We propose to consider subspaces in the feature space and assign a weight to each feature to determine the contribution of that feature in different subspaces to modeling and recognizing patterns. Weights can be computed if a learning estimation method such as maximum likelihood is given. Experimental results in network intrusion detection based on the proposed approach show promising results.
    Original languageEnglish
    Title of host publicationIEEE-RIVF International Conference on Computing and Communication Technologies
    Subtitle of host publicationResearch, Innovation and Vision for the Future
    EditorsTru Cao, Ralf-Detlef Kutsche, Akim Demaille
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages43-48
    Number of pages6
    ISBN (Print)9781424445660
    DOIs
    Publication statusPublished - 2009
    EventIEEE-RIVF International Conference on Computing and Communication Technologies - Danang City, Viet Nam
    Duration: 13 Jul 200917 Jul 2009

    Conference

    ConferenceIEEE-RIVF International Conference on Computing and Communication Technologies
    CountryViet Nam
    CityDanang City
    Period13/07/0917/07/09

    Fingerprint

    Intrusion detection
    Hidden Markov models
    Maximum likelihood
    Pattern recognition

    Cite this

    Tran, D., Ma, W., & Sharma, D. (2009). Fuzzy Subspace Hidden Markov Models for Pattern Recognition. In T. Cao, R-D. Kutsche, & A. Demaille (Eds.), IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future (pp. 43-48). United States: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/RIVF.2009.5174640
    Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra. / Fuzzy Subspace Hidden Markov Models for Pattern Recognition. IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future. editor / Tru Cao ; Ralf-Detlef Kutsche ; Akim Demaille. United States : IEEE, Institute of Electrical and Electronics Engineers, 2009. pp. 43-48
    @inproceedings{a7af8c7061a8497d91b2bbf070257d26,
    title = "Fuzzy Subspace Hidden Markov Models for Pattern Recognition",
    abstract = "This paper presents a novel fuzzy subspace-based approach to hidden Markov model. Features extracted from patterns are considered as feature vectors in a multi-dimensional feature space. Current hidden Markov modeling techniques treat features equally, however this assumption may not be true. We propose to consider subspaces in the feature space and assign a weight to each feature to determine the contribution of that feature in different subspaces to modeling and recognizing patterns. Weights can be computed if a learning estimation method such as maximum likelihood is given. Experimental results in network intrusion detection based on the proposed approach show promising results.",
    author = "Dat Tran and Wanli Ma and Dharmendra Sharma",
    year = "2009",
    doi = "10.1109/RIVF.2009.5174640",
    language = "English",
    isbn = "9781424445660",
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    editor = "Tru Cao and Ralf-Detlef Kutsche and Akim Demaille",
    booktitle = "IEEE-RIVF International Conference on Computing and Communication Technologies",
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    address = "United States",

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    Tran, D, Ma, W & Sharma, D 2009, Fuzzy Subspace Hidden Markov Models for Pattern Recognition. in T Cao, R-D Kutsche & A Demaille (eds), IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future. IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 43-48, IEEE-RIVF International Conference on Computing and Communication Technologies, Danang City, Viet Nam, 13/07/09. https://doi.org/10.1109/RIVF.2009.5174640

    Fuzzy Subspace Hidden Markov Models for Pattern Recognition. / Tran, Dat; Ma, Wanli; Sharma, Dharmendra.

    IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future. ed. / Tru Cao; Ralf-Detlef Kutsche; Akim Demaille. United States : IEEE, Institute of Electrical and Electronics Engineers, 2009. p. 43-48.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    N2 - This paper presents a novel fuzzy subspace-based approach to hidden Markov model. Features extracted from patterns are considered as feature vectors in a multi-dimensional feature space. Current hidden Markov modeling techniques treat features equally, however this assumption may not be true. We propose to consider subspaces in the feature space and assign a weight to each feature to determine the contribution of that feature in different subspaces to modeling and recognizing patterns. Weights can be computed if a learning estimation method such as maximum likelihood is given. Experimental results in network intrusion detection based on the proposed approach show promising results.

    AB - This paper presents a novel fuzzy subspace-based approach to hidden Markov model. Features extracted from patterns are considered as feature vectors in a multi-dimensional feature space. Current hidden Markov modeling techniques treat features equally, however this assumption may not be true. We propose to consider subspaces in the feature space and assign a weight to each feature to determine the contribution of that feature in different subspaces to modeling and recognizing patterns. Weights can be computed if a learning estimation method such as maximum likelihood is given. Experimental results in network intrusion detection based on the proposed approach show promising results.

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    SN - 9781424445660

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    BT - IEEE-RIVF International Conference on Computing and Communication Technologies

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    A2 - Kutsche, Ralf-Detlef

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    Tran D, Ma W, Sharma D. Fuzzy Subspace Hidden Markov Models for Pattern Recognition. In Cao T, Kutsche R-D, Demaille A, editors, IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future. United States: IEEE, Institute of Electrical and Electronics Engineers. 2009. p. 43-48 https://doi.org/10.1109/RIVF.2009.5174640