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» Introduction to Randomized Algorithms
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ICDE
2010
IEEE
224views Database» more  ICDE 2010»
16 years 2 months ago
Probabilistic Declarative Information Extraction
Abstract-Unstructured text represents a large fraction of the world's data. It often contain snippets of structured information within them (e.g., people's names and zip ...
Daisy Zhe Wang, Eirinaios Michelakis, Joseph M. He...
SDM
2009
SIAM
105views Data Mining» more  SDM 2009»
16 years 10 days ago
Exploiting Semantic Constraints for Estimating Supersenses with CRFs.
The annotation of words and phrases by ontology concepts is extremely helpful for semantic interpretation. However many ontologies, e.g. WordNet, are too fine-grained and even hu...
Gerhard Paaß, Frank Reichartz
SDM
2009
SIAM
331views Data Mining» more  SDM 2009»
16 years 10 days ago
Privacy Preservation in Social Networks with Sensitive Edge Weights.
With the development of emerging social networks, such as Facebook and MySpace, security and privacy threats arising from social network analysis bring a risk of disclosure of con...
Jie Wang, Jinze Liu, Jun Zhang, Lian Liu
FOIKS
2008
Springer
16 years 3 days ago
Cost-minimising strategies for data labelling : optimal stopping and active learning
Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
Christos Dimitrakakis, Christian Savu-Krohn
122
Voted
ALT
2006
Springer
16 years 3 days ago
How Many Query Superpositions Are Needed to Learn?
Abstract. This paper introduces a framework for quantum exact learning via queries, the so-called quantum protocol. It is shown that usual protocols in the classical learning setti...
Jorge Castro