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Trust Region Newton Methods for Large-Scale Logistic Regression
This video was recorded at 24th Annual International Conference on Machine Learning (ICML), Corvallis 2007. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
