Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended...
We study the potential benefits to classification prediction that arise from having access to unlabeled samples. We compare learning in the semi-supervised model to the standard, ...
Supervised learning uses a training set of labeled examples to compute a classifier which is a mapping from feature vectors to class labels. The success of a learning algorithm i...
Accurate topical classification of user queries allows for increased effectiveness and efficiency in general-purpose web search systems. Such classification becomes critical if th...
Steven M. Beitzel, Eric C. Jensen, Ophir Frieder, ...
Text classification using positive and unlabeled data refers to the problem of building text classifier using positive documents (P) of one class and unlabeled documents (U) of man...