Abstract
Large-scale logistic regression arises in many applications such as document classification and natural language processing. In this paper, we apply a trust region Newton method to maximize the log-likelihood of the logistic regression model. The proposed method uses only approximate Newton steps in the beginning, but achieves fast convergence in the end. Experiments show that it is faster than the commonly used quasi Newton approach for logistic regression. We also compare it with linear SVM implementations.
Keywords
Logistic regressionComputer scienceScale (ratio)Convergence (economics)Logistic model treeNewton's methodSupport vector machineRegressionArtificial intelligenceStatisticsMachine learningMathematicsNonlinear systemGeography
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Publication Info
- Year
- 2007
- Type
- article
- Pages
- 561-568
- Citations
- 286
- Access
- Closed
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Cite This
Chih‐Jen Lin,
Ruby C. Weng,
S. Sathiya Keerthi
(2007).
Trust region Newton methods for large-scale logistic regression.
, 561-568.
https://doi.org/10.1145/1273496.1273567
Identifiers
- DOI
- 10.1145/1273496.1273567