93 search results - page 8 / 19 » Semi-Supervised Text Classification Using Positive and Unlab... |
ICML
2003
IEEE
14 years 8 months ago
2003
IEEE
The problem of learning with positive and unlabeled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labelin...
ML
2000
ACM
13 years 7 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. ...
ICDM
2003
IEEE
14 years 23 days 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...
ECML
2005
Springer
14 years 1 months ago
2005
Springer
Abstract. We study the problem of learning from positive and unlabeled examples. Although several techniques exist for dealing with this problem, they all assume that positive exam...
KDD
2008
ACM
14 years 8 months ago
2008
ACM
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 ...