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» Semi-supervised Learning from General Unlabeled Data
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KDD
2007
ACM
192views Data Mining» more  KDD 2007»
14 years 8 months ago
Active exploration for learning rankings from clickthrough data
We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. ...
Filip Radlinski, Thorsten Joachims
IJON
2010
181views more  IJON 2010»
13 years 6 months ago
Active learning with extremely sparse labeled examples
An active learner usually assumes there are some labeled data available based on which a moderate classifier is learned and then examines unlabeled data to manually label the mos...
Shiliang Sun, David R. Hardoon
TIP
2010
155views more  TIP 2010»
13 years 6 months ago
Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval
—In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels ...
Xiaofei He
FSKD
2008
Springer
174views Fuzzy Logic» more  FSKD 2008»
13 years 8 months ago
A Hybrid Re-sampling Method for SVM Learning from Imbalanced Data Sets
Support Vector Machine (SVM) has been widely studied and shown success in many application fields. However, the performance of SVM drops significantly when it is applied to the pr...
Peng Li, Pei-Li Qiao, Yuan-Chao Liu
KDD
2009
ACM
178views Data Mining» more  KDD 2009»
14 years 8 months ago
Catching the drift: learning broad matches from clickthrough data
Identifying similar keywords, known as broad matches, is an important task in online advertising that has become a standard feature on all major keyword advertising platforms. Eff...
Sonal Gupta, Mikhail Bilenko, Matthew Richardson