Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/120196


Title: Trust region Newton method for large-scale logistic regression.
Authors: Lin, Chih-Jen;Weng, Ruby C.;Keerthi, S. Sathiya
翁久幸
Weng, Ruby C.
Contributors: 應數系
Date: 2007
Issue Date: 2018-09-28 16:30:04 (UTC+8)
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.
Relation: Journal of Machine Learning Research , 9, 627-650
AMS MathSciNet:MR2417250
Data Type: article
DOI 連結: http://dx.doi.org/10.1145/1273496.1273567
Appears in Collections:[應用數學系] 期刊論文

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