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» Practical Preference Relations for Large Data Sets
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ACL
2009
15 years 2 months ago
Multi-Task Transfer Learning for Weakly-Supervised Relation Extraction
Creating labeled training data for relation extraction is expensive. In this paper, we study relation extraction in a special weakly-supervised setting when we have only a few see...
Jing Jiang
BMCBI
2008
95views more  BMCBI 2008»
15 years 5 months ago
Methodology capture: discriminating between the "best" and the rest of community practice
Background: The methodologies we use both enable and help define our research. However, as experimental complexity has increased the choice of appropriate methodologies has become...
James M. Eales, John W. Pinney, Robert D. Stevens,...
ICML
2009
IEEE
16 years 5 months ago
Prototype vector machine for large scale semi-supervised learning
Practical data mining rarely falls exactly into the supervised learning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised...
Kai Zhang, James T. Kwok, Bahram Parvin
ICDM
2006
IEEE
95views Data Mining» more  ICDM 2006»
15 years 11 months ago
TOP-COP: Mining TOP-K Strongly Correlated Pairs in Large Databases
Recently, there has been considerable interest in computing strongly correlated pairs in large databases. Most previous studies require the specification of a minimum correlation...
Hui Xiong, Mark Brodie, Sheng Ma
CORR
2002
Springer
187views Education» more  CORR 2002»
15 years 4 months ago
Answer Sets for Consistent Query Answering in Inconsistent Databases
A relational database is inconsistent if it does not satisfy a given set of integrity constraints. Nevertheless, it is likely that most of the data in it is consistent with the co...
Marcelo Arenas, Leopoldo E. Bertossi, Jan Chomicki