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CORR
2000
Springer
120views Education» more  CORR 2000»
13 years 8 months ago
Scaling Up Inductive Logic Programming by Learning from Interpretations
When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming ...
Hendrik Blockeel, Luc De Raedt, Nico Jacobs, Bart ...
SIGIR
2008
ACM
13 years 9 months ago
Learning query intent from regularized click graphs
This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a ...
Xiao Li, Ye-Yi Wang, Alex Acero
ICDE
2009
IEEE
157views Database» more  ICDE 2009»
14 years 10 months ago
Confidence-Aware Join Algorithms
In uncertain and probabilistic databases, confidence values (or probabilities) are associated with each data item. Confidence values are assigned to query results based on combinin...
Parag Agrawal, Jennifer Widom
KDD
2001
ACM
163views Data Mining» more  KDD 2001»
14 years 9 months ago
Learning to recognize brain specific proteins based on low-level features from on-line prediction servers
During the last decade, the area of bioinformatics has produced an overwhelming amount of data, with the recently published draft of the human genome being the most prominent exam...
Henrik Boström, Joakim Cöster, Lars Aske...
TRECVID
2008
13 years 10 months ago
Learning TRECVID'08 High-Level Features from YouTube
Run No. Run ID Run Description infMAP (%) training on TV08 data 1 IUPR-TV-M SIFT visual words with maximum entropy 6.1 2 IUPR-TV-MF SIFT with maximum entropy, fused with color+tex...
Adrian Ulges, Christian Schulze, Markus Koch, Thom...