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TitleTrust region Newton method for large-scale logistic regression.
CreatorLin, Chih-Jen;Weng, Ruby C.;Keerthi, S. Sathiya
翁久幸
Weng, Ruby C.
Contributor應數系
Date2007
Date Issued28-Sep-2018 16:30:04 (UTC+8)
SummaryLarge-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.
RelationJournal of Machine Learning Research , 9, 627-650
AMS MathSciNet:MR2417250
Typearticle
DOI http://dx.doi.org/10.1145/1273496.1273567
dc.contributor 應數系
dc.creator (作者) Lin, Chih-Jen;Weng, Ruby C.;Keerthi, S. Sathiyaen_US
dc.creator (作者) 翁久幸zh_TW
dc.creator (作者) Weng, Ruby C.en_US
dc.date (日期) 2007
dc.date.accessioned 28-Sep-2018 16:30:04 (UTC+8)-
dc.date.available 28-Sep-2018 16:30:04 (UTC+8)-
dc.date.issued (上傳時間) 28-Sep-2018 16:30:04 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/120196-
dc.description.abstract (摘要) 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.en_US
dc.format.extent 250756 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Journal of Machine Learning Research , 9, 627-650
dc.relation (關聯) AMS MathSciNet:MR2417250
dc.title (題名) Trust region Newton method for large-scale logistic regression.en_US
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1145/1273496.1273567
dc.doi.uri (DOI) http://dx.doi.org/10.1145/1273496.1273567