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» Learning to rank for information retrieval (LR4IR 2008)
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CVPR
2008
IEEE
14 years 10 months ago
Multiple-instance ranking: Learning to rank images for image retrieval
We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training e...
Yang Hu, Mingjing Li, Nenghai Yu
NIPS
2008
13 years 10 months ago
Global Ranking Using Continuous Conditional Random Fields
This paper studies global ranking problem by learning to rank methods. Conventional learning to rank methods are usually designed for `local ranking', in the sense that the r...
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang...
IPM
2008
100views more  IPM 2008»
13 years 8 months ago
Query-level loss functions for information retrieval
Many machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieval. However, since...
Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng W...
CIKM
2008
Springer
13 years 10 months ago
Ranking information resources in peer-to-peer text retrieval: an experimental study
This paper experimentally studies approaches to the problem of ranking information resources w.r.t. user queries in peer-to-peer information retrieval. In distributed environments...
Hans F. Witschel
SIGIR
2011
ACM
12 years 11 months ago
Parallel learning to rank for information retrieval
Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficien...
Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady Wiraw...