| dc.creator (作者) | 翁久幸 | zh_TW |
| dc.creator (作者) | Lin, Chih-Jen ; Weng, Ruby C. ; Keerthi, S. Sathiya | - |
| dc.date (日期) | 2008-06 | en_US |
| dc.date.accessioned | 6-十月-2010 11:20:48 (UTC+8) | - |
| dc.date.available | 6-十月-2010 11:20:48 (UTC+8) | - |
| dc.date.issued (上傳時間) | 6-十月-2010 11:20:48 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/46064 | - |
| 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 extend the proposed method to large-scale L2-loss linear support vector machines (SVM). | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Journal of Machine Learning Research, 9,627-650 | en_US |
| dc.title (題名) | Trust Region Newton Method for Logistic Regression | en_US |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1145/1390681.1390703 | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1145/1390681.1390703 | en_US |
