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» A New Heuristic for Optimizing Large Queries
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CVPR
2001
IEEE
14 years 9 months ago
Optimal Adaptive Learning for Image Retrieval
Learning-enhanced relevance feedback is one of the most promising and active research directions in recent year's content-based image retrieval. However, the existing approac...
Tao Wang, Yong Rui, Shi-Min Hu
SIGMOD
2004
ACM
160views Database» more  SIGMOD 2004»
14 years 7 months ago
Extending Query Rewriting Techniques for Fine-Grained Access Control
Current day database applications, with large numbers of users, require fine-grained access control mechanisms, at the level of individual tuples, not just entire relations/views,...
Shariq Rizvi, Alberto O. Mendelzon, S. Sudarshan, ...
ICLP
2009
Springer
14 years 8 months ago
Using Histograms to Better Answer Queries to Probabilistic Logic Programs
Probabilistic logic programs (PLPs) define a set of probability distribution functions (PDFs) over the set of all Herbrand interpretations of the underlying logical language. When...
Matthias Broecheler, Gerardo I. Simari, V. S. Subr...
KDD
2008
ACM
128views Data Mining» more  KDD 2008»
14 years 8 months ago
Bypass rates: reducing query abandonment using negative inferences
We introduce a new approach to analyzing click logs by examining both the documents that are clicked and those that are bypassed--documents returned higher in the ordering of the ...
Atish Das Sarma, Sreenivas Gollapudi, Samuel Ieong
HICSS
2006
IEEE
163views Biometrics» more  HICSS 2006»
14 years 1 months ago
Learning Ranking vs. Modeling Relevance
The classical (ad hoc) document retrieval problem has been traditionally approached through ranking according to heuristically developed functions (such as tf.idf or bm25) or gene...
Dmitri Roussinov, Weiguo Fan