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    TIP
    2010
    119views more  TIP 2010»
    Trajectory Classification Using Switched Dynamical Hidden Markov Models
    12 years 12 months ago
    Trajectory Classification Using Switched Dynamical Hidden Markov Models
    Download  users.isr.ist.utl.pt
    This paper proposes an approach for recognizing human activities (more specifically, pedestrian trajectories) in video sequences, in a surveillance context. A system for automatic ...
    Jacinto C. Nascimento, Mário A. T. Figueire...
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    BMCBI
    2004
    177views more  BMCBI 2004»
    Gapped alignment of protein sequence motifs through Monte Carlo optimization of a hidden Markov model
    13 years 5 months ago
    Gapped alignment of protein sequence motifs through Monte Carlo optimization of a hidden Markov model
    Download  www.biomedcentral.com
    Background: Certain protein families are highly conserved across distantly related organisms and belong to large and functionally diverse superfamilies. The patterns of conservati...
    Andrew F. Neuwald, Jun S. Liu
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    ICML
    2005
    IEEE
    195views Machine Learning» more  ICML 2005»
    Active learning for Hidden Markov Models: objective functions and algorithms
    14 years 6 months ago
    Active learning for Hidden Markov Models: objective functions and algorithms
    Download  www.cs.cmu.edu
    Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
    Brigham Anderson, Andrew Moore
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    BMCBI
    2004
    208views more  BMCBI 2004»
    Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures
    13 years 5 months ago
    Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures
    Download  www.biomedcentral.com
    Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction...
    Vadim Alexandrov, Mark Gerstein
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    COMPLIFE
    2006
    Springer
    243views Applied Computing» more  COMPLIFE 2006»
    Set-Oriented Dimension Reduction: Localizing Principal Component Analysis Via Hidden Markov Models
    13 years 9 months ago
    Set-Oriented Dimension Reduction: Localizing Principal Component Analysis Via Hidden Markov Models
    Download  biocomputing.mi.fu-berlin.de
    We present a method for simultaneous dimension reduction and metastability analysis of high dimensional time series. The approach is based on the combination of hidden Markov model...
    Illia Horenko, Johannes Schmidt-Ehrenberg, Christo...
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