CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2009 vol.31 Issue No.02 - February

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Issue No.02 - February (2009 vol.31)

pp: 275-287

Shihao Ji , Duke University, Durham

Layne T. Watson , Virginia Polytechnic Institute and State Univeristy, Blacksburg

Lawrence Carin , Duke University, Durham

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.71

ABSTRACT

Hidden Markov model (HMM) classifier design is considered for the analysis of sequential data, incorporating both labeled and unlabeled data for training; the balance between the use of labeled and unlabeled data is controlled by an allocation parameter \lambda \in [0, 1), where \lambda = 0 corresponds to purely supervised HMM learning (based only on the labeled data) and \lambda = 1 corresponds to unsupervised HMM-based clustering (based only on the unlabeled data). The associated estimation problem can typically be reduced to solving a set of fixed-point equations in the form of a ?natural-parameter homotopy.

INDEX TERMS

Semisupervised learning, homotopy method, hidden Markov models (HMMs), supervised learning.

CITATION

Shihao Ji, Layne T. Watson, Lawrence Carin, "Semisupervised Learning of Hidden Markov Models via a Homotopy Method",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.31, no. 2, pp. 275-287, February 2009, doi:10.1109/TPAMI.2008.71REFERENCES