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NIPS
2008
13 years 9 months ago
Empirical performance maximization for linear rank statistics
The ROC curve is known to be the golden standard for measuring performance of a test/scoring statistic regarding its capacity of discrimination between two populations in a wide v...
Stéphan Clémençon, Nicolas Va...
NIPS
2008
13 years 9 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...
TIP
2010
155views more  TIP 2010»
13 years 6 months ago
Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval
—In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels ...
Xiaofei He
ECML
2006
Springer
13 years 11 months ago
Cost-Sensitive Learning of SVM for Ranking
Abstract. In this paper, we propose a new method for learning to rank. `Ranking SVM' is a method for performing the task. It formulizes the problem as that of binary classific...
Jun Xu, Yunbo Cao, Hang Li, Yalou Huang
ECIR
2010
Springer
13 years 9 months ago
Using the Quantum Probability Ranking Principle to Rank Interdependent Documents
A known limitation of the Probability Ranking Principle (PRP) is that it does not cater for dependence between documents. Recently, the Quantum Probability Ranking Principle (QPRP)...
Guido Zuccon, Leif Azzopardi