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» Deductive Algorithmic Knowledge
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156
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CIKM
2010
Springer
15 years 1 months ago
Regularization and feature selection for networked features
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, ...
Hongliang Fei, Brian Quanz, Jun Huan
133
Voted
ECCV
2008
Springer
16 years 5 months ago
SERBoost: Semi-supervised Boosting with Expectation Regularization
The application of semi-supervised learning algorithms to large scale vision problems suffers from the bad scaling behavior of most methods. Based on the Expectation Regularization...
Amir Saffari, Helmut Grabner, Horst Bischof
117
Voted
ICPR
2008
IEEE
16 years 4 months ago
Active query selection for semi-supervised clustering
Semi-supervised clustering allows a user to specify available prior knowledge about the data to improve the clustering performance. A common way to express this information is in ...
Anil K. Jain, Pavan Kumar Mallapragada, Rong Jin
253
Voted
GIS
2005
ACM
16 years 4 months ago
Extracting spatial association rules from spatial transactions
Georeferenced information is growing every day, and geographical information systems are becoming crucial in many decision processes. As a consequence, extracting knowledge from G...
Salvatore Rinzivillo, Franco Turini
149
Voted
ICML
2007
IEEE
16 years 4 months ago
Cross-domain transfer for reinforcement learning
A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcemen...
Matthew E. Taylor, Peter Stone