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Computational Management Science

, Volume 8, Issue 4, pp 415–428 | Cite as

Kernel logistic regression using truncated Newton method

  • Maher MaaloufEmail author
  • Theodore B. Trafalis
  • Indra Adrianto
Original Paper

Abstract

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.

Keywords

Classification Logistic regression Kernel methods Truncated Newton method 

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Maher Maalouf
    • 1
    Email author
  • Theodore B. Trafalis
    • 1
  • Indra Adrianto
    • 1
  1. 1.School of Industrial EngineeringUniversity of OklahomaNormanUSA

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