221 search results - page 20 / 45 » Learning from Labeled and Unlabeled Data Using Random Walks |
MLDM
2007
Springer
14 years 2 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 (...
ICDM
2006
IEEE
14 years 1 months ago
2006
IEEE
In active learning, a machine learning algorithm is given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. ...
SDM
2009
SIAM
14 years 5 months ago
2009
SIAM
The annotation of words and phrases by ontology concepts is extremely helpful for semantic interpretation. However many ontologies, e.g. WordNet, are too fine-grained and even hu...
MICCAI
2010
Springer
13 years 6 months ago
2010
Springer
Abstract. Learning-based approaches have become increasingly practical in medical imaging. For a supervised learning strategy, the quality of the trained algorithm (usually a class...
ML
2010
ACM
13 years 6 months ago
2010
ACM
When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled samples ...