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    BMCBI
    2008
    111views more  BMCBI 2008»
    Comparative optimism in models involving both classical clinical and gene expression information
    15 years 3 months ago
    Comparative optimism in models involving both classical clinical and gene expression information
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    Background: In cancer research, most clinical variables have already been investigated and are now well established. The use of transcriptomic variables has raised two problems: r...
    Caroline Truntzer, Delphine Maucort-Boulch, Pascal...
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    136
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    BMCBI
    2005
    140views more  BMCBI 2005»
    Dissecting systems-wide data using mixture models: application to identify affected cellular processes
    15 years 3 months ago
    Dissecting systems-wide data using mixture models: application to identify affected cellular processes
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    Background: Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many condition...
    J. Peter Svensson, Renée X. de Menezes, Ing...
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    137
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    EACL
    2003
    ACL Anthology
    170views Natural Language Processing» more  EACL 2003»
    A Comparison of Event Models for Naive Bayes Anti-Spam E-Mail Filtering
    15 years 4 months ago
    A Comparison of Event Models for Naive Bayes Anti-Spam E-Mail Filtering
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    We describe experiments with a Naive Bayes text classifier in the context of anti-spam E-mail filtering, using two different statistical event models: a multi-variate Bernoulli ...
    Karl-Michael Schneider
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    278
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    SMA
    2011
    ACM
    265views Solid Modeling» more  SMA 2011»
    Modeling individual migraine severity with autoregressive ordered probit models
    14 years 10 months ago
    Modeling individual migraine severity with autoregressive ordered probit models
    Download  www-m4.ma.tum.de
    This paper considers the problem of modeling migraine severity assessments and their dependence on weather and time characteristics. We take on the viewpoint of a patient who is i...
    Claudia Czado, Anette Heyn, Gernot Müller
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    141
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    ICML
    1999
    IEEE
    163views Machine Learning» more  ICML 1999»
    Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
    16 years 4 months ago
    Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
    Download  www.ri.cmu.edu
    We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
    Sebastian Thrun, John Langford, Dieter Fox
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