In traditional text classification, a classifier is built using labeled training documents of every class. This paper studies a different problem. Given a set P of documents of a ...
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...
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper sh...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...