Fuzzy Subspace Hidden Markov Models for Pattern Recognition

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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

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    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