44 search results - page 2 / 9 » Combining clustering and co-training to enhance text classif... |
ICDM
2003
IEEE
13 years 10 months ago
2003
IEEE
Semi-supervised learning methods construct classifiers using both labeled and unlabeled training data samples. While unlabeled data samples can help to improve the accuracy of trai...
AMT
2006
Springer
13 years 8 months ago
2006
Springer
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...
IJCAI
2003
13 years 6 months ago
2003
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 ...
NIPS
2008
13 years 6 months ago
2008
The cluster assumption is exploited by most semi-supervised learning (SSL) methods. However, if the unlabeled data is merely weakly related to the target classes, it becomes quest...
ML
2000
ACM
13 years 4 months ago
2000
ACM
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. ...