ML Anthology

Trust Region Newton Methods for Large-Scale Logistic Regression

Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi
ICML 2007 pp. 561-568
doi:10.1145/1273496.1273567 /icml/2007/lin2007icml-trust/

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.

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Text

Lin et al. "Trust Region Newton Methods for Large-Scale Logistic Regression." International Conference on Machine Learning, 2007. doi:10.1145/1273496.1273567

Markdown

[Lin et al. "Trust Region Newton Methods for Large-Scale Logistic Regression." International Conference on Machine Learning, 2007.](https://mlanthology.org/icml/2007/lin2007icml-trust/) doi:10.1145/1273496.1273567

BibTeX

@inproceedings{lin2007icml-trust,
  title     = {{Trust Region Newton Methods for Large-Scale Logistic Regression}},
  author    = {Lin, Chih-Jen and Weng, Ruby C. and Keerthi, S. Sathiya},
  booktitle = {International Conference on Machine Learning},
  year      = {2007},
  pages     = {561-568},
  doi       = {10.1145/1273496.1273567},
  url       = {https://mlanthology.org/icml/2007/lin2007icml-trust/}
}