Kernel logistic regression using truncated Newton method

Maher Maalouf, Theodore B. Trafalis, Indra Adrianto

    Research output: Contribution to journalArticlepeer-review

    26 Scopus citations


    Kernel logistic regression (KLR) is a powerful nonlinear classifier. The combination of KLR and the truncated-regularized iteratively re-weighted least-squares (TR-IRLS) algorithm, has led to a powerful classification method using small-to-medium size data sets. This method (algorithm), is called truncated-regularized kernel logistic regression (TR-KLR). Compared to support vector machines (SVM) and TR-IRLS on twelve benchmark publicly available data sets, the proposed TR-KLR algorithm is as accurate as, and much faster than, SVM and more accurate than TR-IRLS. The TR-KLR algorithm also has the advantage of providing direct prediction probabilities.

    Original languageBritish English
    Pages (from-to)415-428
    Number of pages14
    JournalComputational Management Science
    Issue number4
    StatePublished - Nov 2011


    • Classification
    • Kernel methods
    • Logistic regression
    • Truncated Newton method


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