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» Uncertainty Modeling for Expensive Functions: A Rank Transfo...
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PVLDB
2010
97views more  PVLDB 2010»
13 years 2 months ago
Ranking Continuous Probabilistic Datasets
Ranking is a fundamental operation in data analysis and decision support, and plays an even more crucial role if the dataset being explored exhibits uncertainty. This has led to m...
Jian Li, Amol Deshpande
SIGIR
2009
ACM
13 years 10 months ago
Risky business: modeling and exploiting uncertainty in information retrieval
Most retrieval models estimate the relevance of each document to a query and rank the documents accordingly. However, such an approach ignores the uncertainty associated with the ...
Jianhan Zhu, Jun Wang, Ingemar J. Cox, Michael J. ...
SIGIR
2011
ACM
12 years 6 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...
CIVR
2006
Springer
201views Image Analysis» more  CIVR 2006»
13 years 7 months ago
Efficient Margin-Based Rank Learning Algorithms for Information Retrieval
Learning a good ranking function plays a key role for many applications including the task of (multimedia) information retrieval. While there are a few rank learning methods availa...
Rong Yan, Alexander G. Hauptmann
ICDE
2009
IEEE
170views Database» more  ICDE 2009»
14 years 5 months ago
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks
Abstract-- When dealing with massive quantities of data, topk queries are a powerful technique for returning only the k most relevant tuples for inspection, based on a scoring func...
Graham Cormode, Feifei Li, Ke Yi