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    » Large-scale sparse logistic regression
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    PRL
    2011
    174views Computer Networks» more  PRL 2011»
    A sparse version of the ridge logistic regression for large-scale text categorization
    12 years 11 months ago
    A sparse version of the ridge logistic regression for large-scale text categorization
    Download  www-ljk.imag.fr
    The ridge logistic regression has successfully been used in text categorization problems and it has been shown to reach the same performance as the Support Vector Machine but with...
    Sujeevan Aseervatham, Anestis Antoniadis, É...
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    ICML
    2003
    IEEE
    182views Machine Learning» more  ICML 2003»
    Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization
    14 years 5 months ago
    Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization
    Download  www.informedia.cs.cmu.edu
    Logistic Regression (LR) has been widely used in statistics for many years, and has received extensive study in machine learning community recently due to its close relations to S...
    Jian Zhang, Rong Jin, Yiming Yang, Alexander G. Ha...
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    SDM
    2009
    SIAM
    179views Data Mining» more  SDM 2009»
    Parallel Large Scale Feature Selection for Logistic Regression.
    14 years 2 months ago
    Parallel Large Scale Feature Selection for Logistic Regression.
    Download  www.cs.umass.edu
    Daria Sorokina, Jeremy Kubica, Sameer Singh, Scott...
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    CIDM
    2007
    IEEE
    138views Artificial Intelligence» more  CIDM 2007»
    Algorithms for Fast Large Scale Data Mining Using Logistic Regression
    13 years 11 months ago
    Algorithms for Fast Large Scale Data Mining Using Logistic Regression
    Download  www.omidrouhani.com
    Omid Rouhani-Kalleh
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    AAAI
    2007
    100views Intelligent Agents» more  AAAI 2007»
    A Method for Large-Scale l1-Regularized Logistic Regression
    13 years 7 months ago
    A Method for Large-Scale l1-Regularized Logistic Regression
    Download  www.stanford.edu
    Kwangmoo Koh, Seung-Jean Kim, Stephen P. Boyd
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