| dc.contributor | 應數系 | |
| dc.creator (作者) | Lin, Chih-Jen;Weng, Ruby C.;Keerthi, S. Sathiya | en_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 | |
