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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