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» The Hardness of Metric Labeling
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ICCPOL
2009
Springer
13 years 5 months ago
A Simple and Efficient Model Pruning Method for Conditional Random Fields
Conditional random fields (CRFs) have been quite successful in various machine learning tasks. However, as larger and larger data become acceptable for the current computational ma...
Hai Zhao, Chunyu Kit
TIT
2011
109views more  TIT 2011»
13 years 2 months ago
On Achieving Local View Capacity Via Maximal Independent Graph Scheduling
—“If we know more, we can achieve more.” This adage also applies to networks, where more information about the network state translates into higher sum-rates. In this paper, ...
Vaneet Aggarwal, Amir Salman Avestimehr, Ashutosh ...
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
14 years 8 months ago
Practical learning from one-sided feedback
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...
D. Sculley
SENSYS
2003
ACM
14 years 1 months ago
GEM: graph eMbedding for routing and data-centric storage in sensor networks without geographic information
The widespread deployment of sensor networks is on the horizon. One of the main challenges in sensor networks is to process and aggregate data in the network rather than wasting e...
James Newsome, Dawn Xiaodong Song
CIKM
2008
Springer
13 years 10 months ago
Intra-document structural frequency features for semi-supervised domain adaptation
In this work we try to bridge the gap often encountered by researchers who find themselves with few or no labeled examples from their desired target domain, yet still have access ...
Andrew Arnold, William W. Cohen