Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/120196
DC Field | Value | Language |
---|---|---|
dc.contributor | 應數系 | |
dc.creator | Lin, Chih-Jen;Weng, Ruby C.;Keerthi, S. Sathiya | en_US |
dc.creator | 翁久幸 | zh_TW |
dc.creator | Weng, Ruby C. | en_US |
dc.date | 2007 | |
dc.date.accessioned | 2018-09-28T08:30:04Z | - |
dc.date.available | 2018-09-28T08:30:04Z | - |
dc.date.issued | 2018-09-28T08:30:04Z | - |
dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/120196 | - |
dc.description.abstract | 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 | Journal of Machine Learning Research , 9, 627-650 | |
dc.relation | AMS MathSciNet:MR2417250 | |
dc.title | Trust region Newton method for large-scale logistic regression. | en_US |
dc.type | article | |
dc.identifier.doi | 10.1145/1273496.1273567 | |
dc.doi.uri | http://dx.doi.org/10.1145/1273496.1273567 | |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | restricted | - |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | 期刊論文 |
Files in This Item:
File | Description | Size | Format | |
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p561-lin.pdf | 244.88 kB | Adobe PDF2 | View/Open |
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