| dc.contributor | 統計系 | - |
| dc.creator (作者) | Lin, C.-J.;Weng, Ruby Chiu-Hsing;Keerthi, S.S. | - |
| dc.creator (作者) | 翁久幸 | - |
| dc.date (日期) | 2007 | - |
| dc.date.accessioned | 13-Jul-2015 15:16:50 (UTC+8) | - |
| dc.date.available | 13-Jul-2015 15:16:50 (UTC+8) | - |
| dc.date.issued (上傳時間) | 13-Jul-2015 15:16:50 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/76496 | - |
| 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. | - |
| dc.format.extent | 176 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | ACM International Conference Proceeding Series,Volume 227, Pages 561-568 | - |
| dc.relation (關聯) | 24th International Conference on Machine Learning, ICML 2007,20 June 2007 through 24 June 2007,Corvalis, OR | - |
| dc.subject (關鍵詞) | 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 | - |
| dc.title (題名) | Trust region Newton methods for large-scale logistic regression | - |
| dc.type (資料類型) | conference | en |
| dc.identifier.doi (DOI) | 10.1145/1273496.1273567 | - |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1145/1273496.1273567 | - |
