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» Learning to rank for information retrieval (LR4IR 2008)
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CIVR
2008
Springer
166views Image Analysis» more  CIVR 2008»
15 years 8 months ago
A probabilistic ranking framework using unobservable binary events for video search
Recent content-based video retrieval systems combine output of concept detectors (also known as high-level features) with text obtained through automatic speech recognition. This ...
Robin Aly, Djoerd Hiemstra, Arjen P. de Vries, Fra...
ECIR
2007
Springer
15 years 7 months ago
A Bayesian Approach for Learning Document Type Relevance
Retrieval accuracy can be improved by considering which document type should be filtered out and which should be ranked higher in the result list. Hence, document type can be used...
Peter C. K. Yeung, Stefan Büttcher, Charles L...
CVPR
2007
IEEE
15 years 10 months ago
Diverse Active Ranking for Multimedia Search
Interactively learning from a small sample of unlabeled examples is an enormously challenging task, one that often arises in vision applications. Relevance feedback and more recen...
ShyamSundar Rajaram, Charlie K. Dagli, Nemanja Pet...
SIGIR
2010
ACM
15 years 10 months ago
Learning more powerful test statistics for click-based retrieval evaluation
Interleaving experiments are an attractive methodology for evaluating retrieval functions through implicit feedback. Designed as a blind and unbiased test for eliciting a preferen...
Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, T...
SIGIR
2009
ACM
16 years 19 days ago
Reducing long queries using query quality predictors
Long queries frequently contain many extraneous terms that hinder retrieval of relevant documents. We present techniques to reduce long queries to more effective shorter ones tha...
Giridhar Kumaran, Vitor R. Carvalho