297 search results - page 30 / 60 » Learning from Ambiguously Labeled Examples |
TCS
2011
13 years 5 months ago
2011
The PAC-learning model is distribution-independent in the sense that the learner must reach a learning goal with a limited number of labeled random examples without any prior know...
CVPR
2007
IEEE
15 years 29 days ago
2007
IEEE
Current methods for learning visual categories work well when a large amount of labeled data is available, but can run into severe difficulties when the number of labeled examples...
ISDA
2010
IEEE
13 years 8 months ago
2010
IEEE
Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature sel...
MLDM
2007
Springer
14 years 5 months ago
2007
Springer
Transduction is an inference mechanism “from particular to particular”. Its application to classification tasks implies the use of both labeled (training) data and unlabeled (...
ECCV
2010
Springer
13 years 11 months ago
2010
Springer
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...