Trust Region Newton Method for Logistic Regression

Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi

JMLR 2008 pp. 627-650

/jmlr/2008/lin2008jmlr-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 extend the proposed method to large-scale L2-loss linear support vector machines (SVM).

PDF JMLR Semantic Scholar

Cite

Text

Lin et al. "Trust Region Newton Method for Logistic Regression." Journal of Machine Learning Research, 2008.

Markdown

[Lin et al. "Trust Region Newton Method for Logistic Regression." Journal of Machine Learning Research, 2008.](https://mlanthology.org/jmlr/2008/lin2008jmlr-trust/)

BibTeX

@article{lin2008jmlr-trust,
  title     = {{Trust Region Newton Method for Logistic Regression}},
  author    = {Lin, Chih-Jen and Weng, Ruby C. and Keerthi, S. Sathiya},
  journal   = {Journal of Machine Learning Research},
  year      = {2008},
  pages     = {627-650},
  volume    = {9},
  url       = {https://mlanthology.org/jmlr/2008/lin2008jmlr-trust/}
}