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https://ah.lib.nccu.edu.tw/handle/140.119/120196
題名: | Trust region Newton method for large-scale logistic regression. | 作者: | Lin, Chih-Jen;Weng, Ruby C.;Keerthi, S. Sathiya 翁久幸 Weng, Ruby C. |
貢獻者: | 應數系 | 日期: | 2007 | 上傳時間: | 28-九月-2018 | 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. | 關聯: | Journal of Machine Learning Research , 9, 627-650 AMS MathSciNet:MR2417250 |
資料類型: | article | DOI: | http://dx.doi.org/10.1145/1273496.1273567 |
Appears in Collections: | 期刊論文 |
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