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Title:	Trust region Newton methods for large-scale logistic regression
Authors:	Lin, C.-J.;Weng, Ruby Chiu-Hsing;Keerthi, S.S. 翁久幸
Contributors:	統計系
Keywords:	Approximation algorithms;Classification (of information);Convergence of numerical methods;Mathematical models;Natural language processing systems;Regression analysis;Logistic regression;Quasi Newton approach;Newton-Raphson method
Date:	2007
Issue Date:	2015-07-13 15:16:50 (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:	ACM International Conference Proceeding Series,Volume 227, Pages 561-568 24th International Conference on Machine Learning, ICML 2007,20 June 2007 through 24 June 2007,Corvalis, OR
Data Type:	conference
DOI 連結:	http://dx.doi.org/10.1145/1273496.1273567
DOI:	10.1145/1273496.1273567
Appears in Collections:	[統計學系] 會議論文

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