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ML
2010
ACM
185views Machine Learning» more  ML 2010»
14 years 11 months ago
Learning to rank on graphs
Graph representations of data are increasingly common. Such representations arise in a variety of applications, including computational biology, social network analysis, web applic...
Shivani Agarwal
SDM
2011
SIAM
414views Data Mining» more  SDM 2011»
14 years 7 months ago
Clustered low rank approximation of graphs in information science applications
In this paper we present a fast and accurate procedure called clustered low rank matrix approximation for massive graphs. The procedure involves a fast clustering of the graph and...
Berkant Savas, Inderjit S. Dhillon
SIGIR
2011
ACM
14 years 7 months ago
Pseudo test collections for learning web search ranking functions
Test collections are the primary drivers of progress in information retrieval. They provide a yardstick for assessing the effectiveness of ranking functions in an automatic, rapi...
Nima Asadi, Donald Metzler, Tamer Elsayed, Jimmy L...
WSDM
2010
ACM
210views Data Mining» more  WSDM 2010»
16 years 1 months ago
Towards Recency Ranking in Web Search
In web search, recency ranking refers to ranking documents by relevance which takes freshness into account. In this paper, we propose a retrieval system which automatically detect...
Anlei Dong, Yi Chang, Zhaohui Zheng, Gilad Mishne,...
FGR
2008
IEEE
132views Biometrics» more  FGR 2008»
15 years 11 months ago
Logit-RankBoost with pruning for face recognition
In this paper a novel ranking-based face recognition (FR) scheme is proposed. Compared with classical twoclass (intra/extra person) and multi-class (each person a single class) sc...
Bangpeng Yao, Haizhou Ai, Shihong Lao