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» Learning from Ambiguously Labeled Examples
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ICML
2007
IEEE
14 years 11 months ago
Sparse probabilistic classifiers
The scores returned by support vector machines are often used as a confidence measures in the classification of new examples. However, there is no theoretical argument sustaining ...
Romain Hérault, Yves Grandvalet
ICMCS
2010
IEEE
198views Multimedia» more  ICMCS 2010»
13 years 12 months ago
Naming persons in news video with label propagation
Labeling persons appearing in video frames with names detected from the video transcript helps improving the video content identification and search task. We develop a face naming...
Phi The Pham, Marie-Francine Moens, Tinne Tuytelaa...
CVPR
2010
IEEE
13 years 9 months ago
P-N learning: Bootstrapping binary classifiers by structural constraints
This paper shows that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if knowing the ...
Zdenek Kalal, Jiri Matas, Krystian Mikolajczyk
ICDM
2010
IEEE
128views Data Mining» more  ICDM 2010»
13 years 9 months ago
User-Based Active Learning
Active learning has been proven a reliable strategy to reduce manual efforts in training data labeling. Such strategies incorporate the user as oracle: the classifier selects the m...
Christin Seifert, Michael Granitzer
COLING
1992
14 years 2 days ago
Learning Translation Templates From Bilingual Text
This paper proposes a two-phase example-based machine translation methodology which develops translation templates from examples and then translates using template matching. This ...
Hiroyuki Kaji, Yuuko Kida, Yasutsugu Morimoto