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政大典藏 > College of Science > Department of Mathematical Sciences > Periodical Articles > Item 140.119/120196

Please use this identifier to cite or link to this item: https://nccur.lib.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 link:	http://dx.doi.org/10.1145/1273496.1273567
DOI:	10.1145/1273496.1273567
Appears in Collections:	[Department of Mathematical Sciences] Periodical Articles

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